Cloud computing resource scheduling method and system based on artificial intelligence

Abstract

The invention relates to the technical field of cloud computing resource scheduling, and discloses a cloud computing resource scheduling method and system based on artificial intelligence, and the system comprises a cloud computing platform information collection module, a cloud computing platform analysis module, and a cloud computing resource scheduling module. According to priorities of a user side computing power resource demand, a communication network speed demand and an optimal spatial position demand, combining user side computing power resource demand information, user side spatial position coordinate information, a user side cloud computing communication transmission demand network speed interval and a computing power resource margin of a cloud computing platform; the optimal cloud computing platform meeting the computing power network speed position requirement of the user side is screened out through grading and step-by-step refined screening of the communication network speed and the spatial position coordinate parameters of the cloud computing platform server, the reasonability and scientificity of cloud computing resource scheduling are achieved, and the utilization rate of cloud computing resources is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing resource scheduling, and in particular to a cloud computing resource scheduling method and system based on artificial intelligence. Background Art

[0002] Cloud computing resource scheduling refers to how to rationally allocate and schedule resources across different types of computing, storage, and network resources to achieve efficient cloud computing resource management and task execution. The goal of resource scheduling is to improve system availability, reliability, and performance. Resources in cloud environments are often dynamic, including changes in availability, delays in resource availability, and fluctuations in resource usage. Scheduling decisions must also consider multiple factors, including task priority, resource requirements, resource types, resource availability, and other constraints. Existing cloud computing resource scheduling cannot intelligently select the optimal cloud computing platform for users in real time based on the platform's computing power, network speed, and server location parameters, reducing both cloud computing resource utilization and the quality of cloud computing services.

[0003] The Chinese invention patent with the announcement number CN118819870B discloses a method and system for implementing resource scheduling based on cloud computing. The method obtains resource usage requirements of resource users for resources to be scheduled, collects resource usage data of the resources to be scheduled, and constructs a resource prediction model for the resources to be scheduled. The resource prediction model is used to predict the resource usage status of the resources to be scheduled, and the tasks to be scheduled for the resources to be scheduled are analyzed. The resource constraints of the resources to be scheduled are analyzed, and the task priorities and task dependencies of the tasks to be scheduled are analyzed to construct a resource scheduling strategy for the resources to be scheduled. A distributed scheduling architecture for the resources to be scheduled is constructed, and redundant task nodes of the distributed scheduling architecture are established. A communication protocol stack for the task nodes and the redundant task nodes is constructed, and the resource scheduling strategy is distributed to the task nodes to obtain resource scheduling task nodes, and the resource scheduling of the resources to be scheduled is executed. The above technical solution cannot optimize the cloud computing platform for user-side scheduling based on the computing power resource margin, communication network speed, and server spatial location parameters of the cloud computing platform. Summary of the Invention

[0004] (1) Technical problems solved In order to solve the problem that the above-mentioned existing cloud computing resource scheduling cannot intelligently screen the optimal cloud computing platform for the user end in real time based on the computing power resource margin, communication network speed, and server space location parameters of the cloud computing platform, thereby reducing the cloud computing resource utilization and cloud computing service quality, the above-mentioned real-time intelligent search for operating cloud computing platform objects, scientific screening of the optimal cloud computing platform based on computing power resource margin, communication network speed, and server space location parameters, real-time and accurate scheduling of the optimal cloud computing platform for the client, and improving the cloud computing resource utilization and cloud computing service quality are achieved.

[0005] (2) Technical solution The present invention is implemented through the following technical solution: a cloud computing resource scheduling method based on artificial intelligence, the method comprising the following steps: S1. Collect the computing resource demand data and spatial location coordinate data of the user terminal; S2. Search and process characteristic information of the cloud computing platform in operation status based on the Internet platform to generate characteristic text data of the cloud computing platform in operation status; S3. Collect and process the computing power resource margin, communication network speed, and spatial location coordinates of the cloud computing platform server based on the operational cloud computing platform characteristic text data, and generate operational cloud computing platform computing power resource margin data, operational cloud computing platform communication network speed data, and operational cloud computing platform server spatial location coordinates data, respectively; S4. Performing a cloud computing platform analysis process to meet the user-side computing resource demand based on the user-side computing resource demand data and the operational cloud computing platform computing resource surplus data, thereby generating user-side computing resource demand cloud computing platform analysis data; S5. Based on the user-side computing power demand cloud computing platform analysis data, the operational cloud computing platform communication network speed data, and the user-side cloud computing communication transmission demand network speed range, perform a cloud computing platform evaluation process to determine whether the cloud computing platform meets the user-side computing power resource and communication network speed requirements, thereby generating user-side computing power network speed demand cloud computing platform evaluation data. S6. Performing a cloud computing platform analysis and processing based on the user-side computing power and network speed demand cloud computing platform assessment data, the operational cloud computing platform server spatial location coordinate data, and the user-side spatial location coordinate data to meet the user-side computing power resources, communication network speed, and spatial location requirements, thereby generating user-side computing power and network speed location demand cloud computing platform analysis data. S7. Construct cloud computing resource scheduling analysis data and execute cloud computing resource scheduling operations.

[0006] Preferably, the steps of collecting the user-side computing resource demand data and the user-side spatial position coordinate data are as follows: S11. Collect the computing power resource demand parameters of the user-side data to be processed in real time online through the cloud computing scheduling platform, and generate the computing power resource demand data of the user-side ,in The unit is floating point operations per second; The cloud computing scheduling platform collects the geographic spatial coordinate parameters of the cloud computing user end in real time and generates the user end spatial location coordinate data. , the user terminal spatial position coordinate data Includes the longitude, latitude and altitude of the user's geographic location.

[0007] Preferably, the steps of searching and processing the characteristic information of the cloud computing platform in operation status based on the Internet platform to generate characteristic text data of the cloud computing platform in operation status are as follows: S21. Use the Rabin-Karp algorithm to search online on the Internet platform according to the cloud computing platform keywords to obtain the product feature information of all cloud computing platforms currently in operation, and generate a feature text data set of the cloud computing platforms in operation. , ;in Indicates the The characteristic text data of the operational cloud computing platform corresponding to each cloud computing platform, Indicates the maximum number of cloud computing platforms; the operational cloud computing platform characteristic text data includes product name text information, provider text information, product type text information and product function text information of the cloud computing platform, and the cloud computing platforms include Alibaba Cloud, Huawei Cloud and Tencent Cloud; the Internet platform includes any one of Baidu Internet Platform, 360 Internet Platform and Google Internet Platform.

[0008] Preferably, the steps for collecting and processing the computing power resource margin, communication network speed, and spatial location coordinates of the cloud computing platform server based on the operational cloud computing platform characteristic text data, and respectively generating the operational cloud computing platform computing power resource margin data, the operational cloud computing platform communication network speed data, and the operational cloud computing platform server spatial location coordinates data are as follows: S31, through the cloud computing scheduling platform according to the operational state cloud computing platform feature text data set Characteristic text data of the cloud computing platform in operation The corresponding cloud computing platform characteristic information collects the computing power resource margin information of the cloud computing platform in real time online, and generates a data set of computing power resource margin of the operational cloud computing platform ,in Indicates the The computing power resource surplus data of the operational cloud computing platform corresponding to the cloud computing platform, The unit is floating point operations per second. The remaining computing power resources of the cloud computing platform in operation state represents the remaining computing power resources parameters that the cloud computing platform in operation state can currently provide to customers for data calculation and processing. Through the cloud computing scheduling platform according to the operational state cloud computing platform feature text data set Characteristic text data of the cloud computing platform in operation The corresponding cloud computing platform characteristic information collects the communication network speed information of the cloud computing platform in real time online, and generates the communication network speed data set of the operational cloud computing platform ,in Indicates the The communication network speed data of the operational cloud computing platform corresponding to the cloud computing platform, The unit is bytes per second. The communication network speed data of the operational cloud computing platform represents the communication network speed parameters that the operational cloud computing platform can currently provide to customers for data transmission. Through the cloud computing scheduling platform according to the operational state cloud computing platform feature text data set Characteristic text data of the cloud computing platform in operation The corresponding cloud computing platform feature information collects the geographic spatial location coordinate information of the cloud computing platform server in real time online, and generates the operational cloud computing platform server spatial location coordinate data set ,in Indicates the The spatial location coordinate data of the cloud computing platform server in operation state corresponding to each cloud computing platform, the spatial location coordinate data of the cloud computing platform server in operation state represents the geographic spatial location coordinate information of the server hardware currently located by the cloud computing platform in operation state; the spatial location coordinate data of the cloud computing platform server in operation state includes the longitude, latitude and poster height of the cloud computing platform server.

[0009] Preferably, the steps of performing a cloud computing platform analysis process to meet the user-side computing resource demand based on the user-side computing resource demand data and the operational cloud computing platform computing resource surplus data to generate the user-side computing resource demand cloud computing platform analysis data are as follows: S41, the user terminal computing power resource demand data The computing power resource surplus data set of the operational cloud computing platform The remaining computing power data of the cloud computing platform in the operational state Compare the computing power resource values ​​and search for computing power resource values ​​that are not less than the computing power resource demand data of the user end. Matching the remaining computing power data of the operational cloud computing platform The corresponding cloud computing platform quantity number text information, and generate the user-side computing power demand cloud computing platform analysis data set , execute and generate the user terminal computing power demand cloud computing platform analysis data set The specific steps are as follows: S411, initialize algorithm parameters, population size N, maximum number of iterations T; S412: Initialize the population, calculate the fitness, and determine the cloud computing platform search pathfinder and the cloud computing platform search follower; S413, according to the position formula Collecting the remaining computing power data of the cloud computing platform in the operational state Update the cloud computing platform search pathfinder position in the search space of , where t represents the current iteration number of the algorithm; Represents the pathfinder searched by the cloud computing platform after the tth iteration The computing power resource surplus data set of the operational cloud computing platform The position in the search space of Indicates the pathfinder searched by the cloud computing platform after the t-1th iteration The computing power resource surplus data set of the operational cloud computing platform The position in the search space, Indicates the pathfinder searched by the cloud computing platform after the t+1th iteration The computing power resource surplus data set of the operational cloud computing platform The position in the search space of Search for the step size factor of the pathfinder movement for the cloud computing platform, The value is uniformly distributed within [0,1]; S414, according to the position formula Update the cloud computing platform to search for followers in the operating state cloud computing platform computing power resource surplus data set The position in the search space of Indicates the cloud computing platform searches for followers after the tth iteration The computing power resource surplus data set of the operational cloud computing platform The position in the search space, Indicates that the cloud computing platform searches for followers after the t+1th iteration The computing power resource surplus data set of the operational cloud computing platform The position in the search space of Indicates that other cloud computing platforms search for followers after the tth iteration The computing power resource surplus data set of the operational cloud computing platform The cloud computing platform searches for the position of the follower in the search space. Mobile is not only related to cloud computing platform search pathfinder location Related and searched by other cloud computing platforms for follower locations The impact of Represents the cloud computing platform search followers in the operating state of the cloud computing platform computing power resource surplus data set The position distance parameter in the search space of Represents the remaining data set of computing power resources of the cloud computing platform in the operating state between the cloud computing platform search pathfinder and the cloud computing platform search follower The position distance parameter in the search space of , ; represents the interaction coefficient between followers in the cloud computing platform search, It represents the attraction coefficient of cloud computing platform search pathfinder to cloud computing platform search followers, 、 All are uniformly distributed in [1,2]; The step size factor for the movement of cloud computing platform search followers and other cloud computing platform search followers, The step size factor for the cloud platform search follower and cloud platform search pathfinder movement, 、 All are random numbers in the range [0,1]; S415: Calculate the remaining computing power data set of the operational cloud computing platform The remaining computing power data of the cloud computing platform in the operational state described in the search space and the user-side computing resource demand data The fitness value of the cloud computing platform in the operational state is set Update the search space to find a computing power resource value that is not less than the computing power resource demand data of the user end Match all the computing power resource surplus data of the operational cloud computing platforms Global optimal value; S416: When the maximum number of iterations is met, the output of step S415 and the user terminal computing power resource demand data are Matching the remaining computing power data of the operational cloud computing platform The corresponding cloud computing platform quantity number text information, and data identification is used to generate the user-side computing power demand cloud computing platform analysis data set , ,in Indicates the The cloud computing platform analyzes the computing power demand of the user end corresponding to each cloud computing platform. Indicates the The user-side computing power demand cloud computing platform analysis data corresponding to the cloud computing platforms is provided, and the user-side computing power demand cloud computing platform analysis data represents the quantity and number text information of the cloud computing platforms that meet the user-side computing power resource requirements.

[0010] Preferably, the cloud computing platform evaluation process that meets the user-side computing power resource and communication network speed requirements is performed based on the user-side computing power demand cloud computing platform analysis data, the operational cloud computing platform communication network speed data, and the user-side cloud computing communication transmission demand network speed range. The operation steps for generating the user-side computing power network speed demand cloud computing platform evaluation data are as follows: S51. Establish the network speed range required for user-side cloud computing communication transmission ,in and They represent the minimum network speed data required for cloud computing communication transmission at the user end and the maximum network speed data required for cloud computing communication transmission at the user end, and The unit of data is bytes per second; S52: Using an iterative deepening search algorithm to analyze the data set according to the computing power requirements of the user terminal cloud computing platform The cloud computing platform analysis data of the user-side computing power requirements described in to The corresponding cloud computing platform quantity number text information is in the communication network speed data set of the operating cloud computing platform Search the search space to find the communication network speed data of the operating state cloud computing platform corresponding to all cloud computing platforms And search out all the communication network speed data of the operating cloud computing platform The network speed range required for communication and transmission with the user-side cloud computing The minimum network speed data required for user-side cloud computing communication transmission The maximum network speed data required for the user-side cloud computing communication transmission Compare the communication network speed values ​​and search for the communication network speed value that belongs to the network speed range required by the user-side cloud computing communication transmission. All the communication network speed data of the operational cloud computing platforms The corresponding cloud computing platform quantity number text information, and data identification is used to generate the user-side computing power and network speed demand cloud computing platform evaluation data set , ,in Indicates the Cloud computing platform evaluation data on user-side computing power and network speed requirements corresponding to each cloud computing platform, Indicates the The user-side computing power and network speed demand cloud computing platform evaluation data corresponding to the cloud computing platform represents the number and number text information of the cloud computing platforms that simultaneously meet the user-side computing power resource requirements and communication network speed requirements.

[0011] Preferably, based on the user-side computing power and network speed demand cloud computing platform evaluation data, the operational cloud computing platform server spatial location coordinate data, and the user-side spatial location coordinate data, a cloud computing platform analysis process is performed to meet the user-side computing power resources, communication network speed, and spatial location requirements. The operating steps for generating the user-side computing power and network speed location demand cloud computing platform analysis data are as follows: S61: Using a depth-restricted search algorithm to evaluate the data set based on the computing power and network speed requirements of the user terminal. The cloud computing platform evaluation data for user-side computing power and network speed requirements described in to The corresponding cloud computing platform quantity number text information is in the spatial location coordinate data set of the operating cloud computing platform server Search the search space to find the spatial location coordinate data of the operating state cloud computing platform server corresponding to all cloud computing platforms and the searched out spatial location coordinate data of the operational cloud computing platform server Respectively with the user terminal spatial position coordinate data Combined with the spatial distance formula, the spatial distance between the user end and the cloud computing platform is numerically processed to measure the spatial distance data set between the user end and the cloud computing platform. ,in Indicates the The spatial distance data from the user end to the cloud computing platform corresponding to the cloud computing platform to the user end, Indicates the The spatial distance data from the user end to the cloud computing platform corresponding to the cloud computing platform to the user end, and The unit is meter; S62, using a unified cost search algorithm to collect spatial distance data from the user terminal to the cloud computing platform Search for the cloud computing platform quantity number text information corresponding to the spatial distance data from the user terminal to the cloud computing platform with the smallest spatial distance value, and generate the user terminal computing power, network speed and location demand cloud computing platform analysis data through data identification. The user-side computing power, network speed and location requirement cloud computing platform analysis data represents the quantity and number text information of cloud computing platforms that simultaneously meet the user-side computing power resource requirements, communication network speed requirements and spatial location requirements.

[0012] Preferably, the steps of constructing the cloud computing resource scheduling analysis data and executing the cloud computing resource scheduling operation are as follows: S71, the user terminal computing power resource demand data , the characteristic text data set of the operational cloud computing platform And the cloud computing platform analysis data of the user's computing power, network speed and location requirements Perform data combination identification to construct cloud computing resource scheduling analysis data ,in ; S72: The cloud computing scheduling platform analyzes data according to the cloud computing resource scheduling. The cloud computing platform analysis data of the user-side computing power, network speed and location requirements mentioned in The corresponding cloud computing platform quantity number text information, and the operational cloud computing platform feature text data set Search the search space to find the cloud computing platform analysis data that matches the user's computing power, network speed, and location requirements. The corresponding operational cloud computing platform feature text data ; S73, the cloud computing scheduling platform searches for the characteristic text data of the operating state cloud computing platform in step S72 Control the corresponding cloud computing platform according to the user's computing power resource demand data Execute cloud computing resource scheduling jobs for the user side.

[0013] An artificial intelligence-based cloud computing resource scheduling system, used to implement the artificial intelligence-based cloud computing resource scheduling method, the system includes a cloud computing platform information collection module, a cloud computing platform analysis module, and a cloud computing resource scheduling module; The cloud computing platform information collection module includes a user-side computing resource demand collection unit, a user-side spatial location collection unit, and an operational cloud computing platform feature information search unit; The user-side computing power resource demand collection unit collects user-side computing power resource demand data through the cloud computing scheduling platform; the user-side spatial position collection unit collects user-side spatial position coordinate data through the cloud computing scheduling platform; the operational cloud computing platform feature information search unit searches for and processes the operational cloud computing platform feature information based on the Internet platform to generate operational cloud computing platform feature text data; The cloud computing platform analysis module includes an operational cloud computing platform computing power resource margin collection unit, an operational cloud computing platform communication network speed collection unit, an operational cloud computing platform server spatial location collection unit, a cloud computing platform computing power resource analysis unit, a user-side cloud computing communication transmission required network speed interval storage unit, a cloud computing platform communication network speed evaluation unit, and a cloud computing platform server spatial location analysis unit; The computing power resource surplus collection unit of the operational cloud computing platform collects and processes the computing power resource surplus of the cloud computing platform according to the characteristic text data of the operational cloud computing platform in combination with the cloud computing scheduling platform, and generates computing power resource surplus data of the operational cloud computing platform; the communication network speed collection unit of the operational cloud computing platform collects and processes the communication network speed of the cloud computing platform according to the characteristic text data of the operational cloud computing platform in combination with the cloud computing scheduling platform, and generates communication network speed data of the operational cloud computing platform; the spatial position collection unit of the operational cloud computing platform server collects and processes the spatial position coordinates of the cloud computing platform server according to the characteristic text data of the operational cloud computing platform in combination with the cloud computing scheduling platform, and generates spatial position coordinate data of the operational cloud computing platform server; the computing power resource analysis unit of the cloud computing platform performs cloud computing to meet the computing power resource demand of the user end according to the computing power resource demand data of the user end and the computing power resource surplus data of the operational cloud computing platform. The computing platform analyzes and processes the computing power demand of the user-side cloud computing platform to generate the computing power demand of the user-side cloud computing platform analysis data; the user-side cloud computing communication transmission demand network speed interval storage unit is used to store the user-side cloud computing communication transmission demand network speed interval; the cloud computing platform communication network speed evaluation unit performs a computing power demand cloud computing platform evaluation process to meet the computing power resource and communication network speed requirements of the user-side based on the computing power demand cloud computing platform analysis data of the user-side, the operational cloud computing platform communication network speed data and the user-side cloud computing communication transmission demand network speed interval, and generates the computing power network speed demand cloud computing platform evaluation data of the user-side; the cloud computing platform server spatial position analysis unit performs a computing power demand cloud computing platform analysis process to meet the computing power resource, communication network speed and spatial position requirements of the user-side based on the computing power network speed demand cloud computing platform evaluation data of the user-side, the operational cloud computing platform server spatial position coordinate data and the user-side spatial position coordinate data, and generates the computing power network speed position demand cloud computing platform analysis data of the user-side; The cloud computing resource scheduling module includes a cloud computing resource scheduling parameter construction unit and a cloud computing resource scheduling job execution unit; The cloud computing resource scheduling parameter construction unit constructs cloud computing resource scheduling analysis data based on the user-side computing power resource demand parameters and the characteristic information of the optimal cloud computing platform required by the user-side; the cloud computing resource scheduling job execution unit executes the cloud computing resource scheduling job based on the cloud computing resource scheduling analysis data and in combination with the cloud computing scheduling platform.

[0014] (3) Beneficial effects The present invention provides a cloud computing resource scheduling method and system based on artificial intelligence. It has the following beneficial effects: Through the cloud computing scheduling platform, the computing power resource demand information and spatial location coordinate information of the user end are accurately obtained in real time, providing reliable data support for the subsequent scientific and precise scheduling of the cloud computing platform for the user end, thereby improving the accuracy of cloud computing resource scheduling; based on the Internet platform combined with data retrieval, the characteristic information of the operating status of the cloud computing platform is searched in real time and comprehensively, providing real data support for the subsequent real-time intelligent evaluation of the cloud computing platform that meets the computing power resource demand, communication network speed demand and optimal spatial location demand of the user end, thereby improving the effect of cloud computing resource scheduling.

[0015] By combining the characteristic information of the operational cloud computing platform with the cloud computing scheduling platform to obtain the computing power resource margin, communication network speed and spatial location coordinate parameters of the cloud computing platform in real time and efficiently, the reliability of cloud computing resource scheduling is improved; according to the priority of user-side computing power resource demand, communication network speed demand and optimal spatial location demand, combined with user-side computing power resource demand information, user-side spatial location coordinate information, user-side cloud computing communication transmission demand network speed range and computing power resource margin, communication network speed and spatial location coordinate parameters of the cloud computing platform, the optimal cloud computing platform that meets the user-side computing power network speed location demand is screened out in a hierarchical and step-by-step manner, so as to achieve the rationality and scientificity of cloud computing resource scheduling and improve the utilization rate of cloud computing resources.

[0016] 3. By timely and efficiently constructing cloud computing resource scheduling analysis information based on the user-side computing power resource demand parameters and the characteristic information of the optimal cloud computing platform required by the user, the efficiency and reliability of cloud computing resource scheduling information collection are improved; based on the cloud computing resource scheduling information and combined with the dynamic cloud computing scheduling platform, cloud computing resource scheduling operations are executed in a timely manner to improve the response speed and accuracy of cloud computing resource scheduling operations and improve the quality of cloud computing resource scheduling services. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a module of an artificial intelligence-based cloud computing resource scheduling system provided by the present invention; Figure 2 This is a flowchart of a cloud computing resource scheduling method based on artificial intelligence provided by the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] The embodiments of the cloud computing resource scheduling method and system based on artificial intelligence are as follows: Example 1:

[0020] See also Figure 1 - Figure 2 , a cloud computing resource scheduling method based on artificial intelligence, the method comprising the following steps: S1. Collect the computing resource demand data and spatial location coordinate data of the user terminal; S2. Search and process characteristic information of the cloud computing platform in operation status based on the Internet platform to generate characteristic text data of the cloud computing platform in operation status; S3. Based on the operational cloud computing platform feature text data, collect and process the computing power resource margin, communication network speed, and spatial location coordinates of the cloud computing platform server, and generate operational cloud computing platform computing power resource margin data, operational cloud computing platform communication network speed data, and operational cloud computing platform server spatial location coordinates data, respectively; S4. Performing a cloud computing platform analysis and processing to meet the user-side computing resource demand based on the user-side computing resource demand data and the operational cloud computing platform computing resource surplus data, thereby generating user-side computing resource demand cloud computing platform analysis data; S5. Based on the user-side computing power demand cloud computing platform analysis data, the operational cloud computing platform communication network speed data, and the user-side cloud computing communication transmission demand network speed range, perform an evaluation process on the cloud computing platform that meets the user-side computing power resource and communication network speed requirements, and generate user-side computing power network speed demand cloud computing platform evaluation data. S6. Based on the user-side computing power and network speed demand cloud computing platform evaluation data, the operational cloud computing platform server spatial location coordinate data, and the user-side spatial location coordinate data, perform cloud computing platform analysis and processing to meet the user-side computing power resources, communication network speed, and spatial location requirements, and generate user-side computing power and network speed location demand cloud computing platform analysis data. S7. Construct cloud computing resource scheduling analysis data and execute cloud computing resource scheduling operations.

[0021] For further information, see Figure 1 - Figure 2 The steps for collecting the user-side computing resource demand data and the user-side spatial location coordinate data are as follows: S11. Collect the computing power resource demand parameters of the user-side data to be processed in real time online through the cloud computing scheduling platform, and generate the computing power resource demand data of the user-side ,in The unit is floating point operations per second; The cloud computing scheduling platform collects the geographic spatial coordinate parameters of the cloud computing user end in real time and generates the user end spatial location coordinate data. , user-side spatial position coordinate data Includes the longitude, latitude and altitude of the user's geographic location.

[0022] The steps for searching and processing the characteristic information of the cloud computing platform in operation status based on the Internet platform and generating characteristic text data of the cloud computing platform in operation status are as follows: S21. Use the Rabin-Karp algorithm to search online on the Internet platform according to the cloud computing platform keywords to obtain the product feature information of all cloud computing platforms currently in operation, and generate a feature text data set of the cloud computing platforms in operation. , ;in Indicates the The characteristic text data of the operational cloud computing platform corresponding to each cloud computing platform, Indicates the maximum number of cloud computing platforms; the characteristic text data of operational cloud computing platforms includes product name text information, provider text information, product type text information, and product function text information of the cloud computing platforms. Cloud computing platforms include Alibaba Cloud, Huawei Cloud, and Tencent Cloud; Internet platforms include any of Baidu Internet Platform, 360 Internet Platform, and Google Internet Platform.

[0023] Through the cooperation between the user-side computing power resource demand collection unit and the user-side spatial position collection unit, the cloud computing scheduling platform is used to accurately and in real time obtain the user-side computing power resource demand information and user-side spatial position coordinate information, providing reliable data support for the subsequent scientific and accurate scheduling of the cloud computing platform for the user side, and improving the accuracy of cloud computing resource scheduling; the operational cloud computing platform feature information search unit, based on the Internet platform combined with data retrieval, searches out the operational cloud computing platform feature information in real time and comprehensively, providing real data support for the subsequent real-time intelligent evaluation of the cloud computing platform that meets the user-side computing power resource demand, communication network speed demand and optimal spatial position demand, and improving the effect of cloud computing resource scheduling.

[0024] For further information, see Figure 1 - Figure 2 The steps for collecting and processing the computing power resource margin, communication network speed, and spatial location coordinates of the cloud computing platform server based on the characteristic text data of the operational cloud computing platform are as follows: S31, through the cloud computing scheduling platform based on the operational state cloud computing platform feature text data collection Characteristic text data of China's cloud computing platform The corresponding cloud computing platform characteristic information collects the computing power resource margin information of the cloud computing platform in real time online, and generates a data set of computing power resource margin of the operational cloud computing platform ,in Indicates the The computing power resource surplus data of the operational cloud computing platform corresponding to the cloud computing platform, The unit is floating point operations per second. The remaining computing power resources of the cloud computing platform in operation state represents the remaining computing power resources parameters that the cloud computing platform in operation state can currently provide to customers for data calculation and processing. Through the cloud computing scheduling platform based on the operational cloud computing platform feature text data collection Characteristic text data of China's cloud computing platform The corresponding cloud computing platform characteristic information collects the communication network speed information of the cloud computing platform in real time online, and generates the communication network speed data set of the operational cloud computing platform ,in Indicates the The communication network speed data of the operational cloud computing platform corresponding to the cloud computing platform, The unit is bytes per second. The communication network speed data of the operational cloud computing platform indicates the communication network speed parameters that the operational cloud computing platform can currently provide to customers for data transmission. Through the cloud computing scheduling platform based on the operational cloud computing platform feature text data collection Characteristic text data of China's cloud computing platform The corresponding cloud computing platform feature information collects the geographic spatial location coordinate information of the cloud computing platform server in real time online, and generates the operational cloud computing platform server spatial location coordinate data set ,in Indicates the The spatial location coordinate data of the cloud computing platform server in operation state corresponds to each cloud computing platform. The spatial location coordinate data of the cloud computing platform server in operation state represents the geographic spatial location coordinate information of the server hardware used by the cloud computing platform in operation state; the spatial location coordinate data of the cloud computing platform server in operation state includes the longitude, latitude and poster height of the cloud computing platform server.

[0025] The steps for generating the user-side computing power demand cloud computing platform analysis data are as follows: S41, the user-side computing power resource demand data and the remaining computing power data of the operational cloud computing platform Computing power resource surplus data of China's cloud computing platform Compare the computing power resource values ​​and search for computing power resource values ​​that are not less than the computing power resource demand data on the user side. Matching operational cloud computing platform computing power resource surplus data The corresponding cloud computing platform quantity number text information, and generate the user-side computing power demand cloud computing platform analysis data set , execute and generate the user-side computing power demand cloud computing platform analysis data set The specific steps are as follows: S411, initialize algorithm parameters, population size N, maximum number of iterations T; S412: Initialize the population, calculate the fitness, and determine the cloud computing platform search pathfinder and the cloud computing platform search follower; S413, according to the position formula Collect data on the remaining computing power resources of the cloud computing platform in operation Update the cloud computing platform search pathfinder position in the search space of , where t represents the current iteration number of the algorithm; Represents the pathfinder searched by the cloud computing platform after the tth iteration A collection of computing power resource surplus data on the operational cloud computing platform The position in the search space, Indicates the pathfinder searched by the cloud computing platform after the t-1th iteration A collection of computing power resource surplus data on the operational cloud computing platform The position in the search space of Indicates the pathfinder searched by the cloud computing platform after the t+1th iteration A collection of computing power resource surplus data on the operational cloud computing platform The position in the search space, Search for the step size factor of the pathfinder movement for the cloud computing platform, The value is uniformly distributed within [0,1]; S414, according to the position formula Update the cloud computing platform search follower in the operating state cloud computing platform computing power resource surplus data set The position in the search space of Indicates the cloud computing platform searches for followers after the tth iteration A collection of computing power resource surplus data on the operational cloud computing platform The position in the search space of Indicates that the cloud computing platform searches for followers after the t+1th iteration A collection of computing power resource surplus data on the operational cloud computing platform The position in the search space of Indicates that other cloud computing platforms search for followers after the tth iteration A collection of computing power resource surplus data on the operational cloud computing platform The cloud computing platform searches for the position of the follower in the search space. Mobile is not only related to cloud computing platform search pathfinder location Related and searched by other cloud computing platforms for follower locations The impact of Represents the cloud computing platform search followers in the operating state of the cloud computing platform computing power resource surplus data set The position distance parameter in the search space of Represents the remaining data set of computing power resources of cloud computing platform in operation between cloud computing platform search pathfinder and cloud computing platform search follower The position distance parameter in the search space of , ; represents the interaction coefficient between followers in the cloud computing platform search, It represents the attraction coefficient of cloud computing platform search pathfinder to cloud computing platform search followers, 、 All are uniformly distributed in [1,2]; The step size factor for the movement of cloud computing platform search followers and other cloud computing platform search followers, The step size factor for the cloud platform search follower and cloud platform search pathfinder movement, 、 All are random numbers in the range [0,1]; S415. Compute the remaining computing power data of the cloud computing platform in the operational state The remaining computing power data of the cloud computing platform in the search space of and user-side computing resource demand data The fitness value of the cloud computing platform is set in the operational state. Update the search space to find a computing power resource value that is not less than the user-side computing power resource demand data Matching computing power resource surplus data of all operational cloud computing platforms Global optimal value; S416: When the maximum number of iterations is met, the output of step S415 and the user-side computing resource demand data are Matching operational cloud computing platform computing power resource surplus data The corresponding cloud computing platform quantity number text information, and data identification is used to generate the user-side computing power demand cloud computing platform analysis data set , ,in Indicates the The cloud computing platform analyzes the computing power demand of the user end corresponding to each cloud computing platform. Indicates the The user-side computing power demand cloud computing platform analysis data corresponding to each cloud computing platform represents the number and number text information of the cloud computing platforms that meet the user-side computing power resource requirements.

[0026] Based on the user-side computing power demand cloud computing platform analysis data, the operational cloud computing platform communication network speed data, and the user-side cloud computing communication transmission demand network speed range, the cloud computing platform that meets the user-side computing power resources and communication network speed requirements is evaluated and processed. The steps for generating the user-side computing power network speed demand cloud computing platform evaluation data are as follows: S51. Establish the network speed range required for user-side cloud computing communication transmission ,in and They represent the minimum network speed data required for cloud computing communication transmission at the user end and the maximum network speed data required for cloud computing communication transmission at the user end, and The unit of data is bytes per second; S52, using iterative deepening search algorithm to analyze data sets based on the computing power requirements of the user end cloud computing platform Cloud computing platform analysis data on user-side computing power requirements to The corresponding cloud computing platform quantity number text information in the operational cloud computing platform communication network speed data set Search the search space to find the communication speed data of all cloud computing platforms in operation And search out all the operational cloud computing platform communication speed data The network speed range required for communication and transmission with user-side cloud computing Minimum network speed data required for cloud computing communication transmission at the user end The maximum network speed data required for communication and transmission of cloud computing on the user side Compare the communication network speed values ​​and search for the communication network speed value that belongs to the user-side cloud computing communication transmission speed range All operational cloud computing platform communication network speed data The corresponding cloud computing platform quantity number text information, and data identification is used to generate the user-side computing power and network speed demand cloud computing platform evaluation data set , ,in Indicates the Cloud computing platform evaluation data on user-side computing power and network speed requirements corresponding to each cloud computing platform, Indicates the The user-side computing power and network speed demand cloud computing platform evaluation data corresponding to each cloud computing platform represents the number and number text information of cloud computing platforms that simultaneously meet the user-side computing power resource requirements and communication network speed requirements.

[0027] Based on the cloud computing platform evaluation data of the user-side computing power and network speed requirements, the spatial location coordinate data of the operational cloud computing platform server, and the user-side spatial location coordinate data, the cloud computing platform is analyzed and processed to meet the user-side computing power resources, communication network speed, and spatial location requirements. The steps for generating the cloud computing platform analysis data of the user-side computing power, network speed, and location requirements are as follows: S61. Use a depth-restricted search algorithm to evaluate the data set on the cloud computing platform according to the computing power and network speed requirements of the user end. Evaluation data on cloud computing platforms for mid-range user computing power and network speed requirements to The corresponding cloud computing platform quantity number text information in the operational cloud computing platform server spatial location coordinate data set Search the search space to find the spatial location coordinate data of all cloud computing platforms corresponding to the operational cloud computing platform servers and the searched out operational cloud computing platform server spatial location coordinate data Respectively with the user's spatial position coordinate data Combined with the spatial distance formula, the spatial distance between the user end and the cloud computing platform is numerically processed to measure the spatial distance data set between the user end and the cloud computing platform. ,in Indicates the The spatial distance data from the user end to the cloud computing platform corresponding to the cloud computing platform to the user end, Indicates the The spatial distance data from the user end to the cloud computing platform corresponding to the cloud computing platform to the user end, and The unit is meter; S62, using a unified cost search algorithm to search for spatial distance data sets from the user end to the cloud computing platform Search for the cloud computing platform quantity and number text information corresponding to the spatial distance data from the user end to the cloud computing platform with the smallest spatial distance value, and generate the user end computing power, network speed and location demand cloud computing platform analysis data through data identification. The user-side computing power, network speed and location requirements of the cloud computing platform analysis data represents the number and number text information of the cloud computing platforms that simultaneously meet the user-side computing power resource requirements, communication network speed requirements and spatial location requirements.

[0028] Through the cooperation of the operational cloud computing platform computing power resource surplus collection unit, the operational cloud computing platform communication network speed collection unit and the operational cloud computing platform server spatial location collection unit, the computing power resource surplus, communication network speed and cloud computing platform server spatial location coordinate parameters of the cloud computing platform are obtained in real time and efficiently based on the operational cloud computing platform feature information combined with the cloud computing scheduling platform, thereby improving the reliability of cloud computing resource scheduling; the cloud computing platform computing power resource analysis unit, the cloud computing platform communication network speed evaluation unit and the cloud computing platform server spatial location analysis unit cooperate with each other, according to the user-side computing power resource demand, communication network speed demand and optimal spatial location demand priority combined with the user-side computing power resource demand information, the user-side spatial location coordinate information, the user-side cloud computing communication transmission demand network speed range and the computing power resource surplus, communication network speed and cloud computing platform server spatial location coordinate parameters of the cloud computing platform, hierarchical and step-by-step fine screening is carried out to find the optimal cloud computing platform that meets the user-side computing power network speed and location requirements, thereby achieving the rationality and scientificity of cloud computing resource scheduling and improving the utilization rate of cloud computing resources.

[0029] For further information, see Figure 1 - Figure 2 The steps to construct cloud computing resource scheduling analysis data and execute cloud computing resource scheduling jobs are as follows: S71, the user-side computing power resource demand data , Operational cloud computing platform feature text data collection The cloud computing platform analyzes data on the computing power, network speed, and location requirements of the user end. Perform data combination identification to construct cloud computing resource scheduling analysis data ,in ; S72. Cloud computing scheduling platform analyzes data according to cloud computing resource scheduling Cloud computing platform analysis data on user-side computing power, network speed, and location requirements The corresponding cloud computing platform quantity number text information, and the cloud computing platform feature text data set in operation Search the search space to find the cloud computing platform analysis data that matches the user's computing power, network speed, and location requirements. Corresponding operational cloud computing platform feature text data ; S73, the cloud computing scheduling platform searches for the operational cloud computing platform feature text data in step S72. Control the corresponding cloud computing platform according to the user's computing power resource demand data Execute cloud computing resource scheduling jobs for the user side.

[0030] Through the cloud computing resource scheduling parameter construction unit, cloud computing resource scheduling analysis information is constructed in a timely and efficient manner based on the user-side computing power resource demand parameters and the optimal cloud computing platform characteristic information required by the user side, thereby improving the efficiency and reliability of cloud computing resource scheduling information collection; the cloud computing resource scheduling job execution unit executes cloud computing resource scheduling jobs in a timely manner based on cloud computing resource scheduling information and in combination with the cloud computing scheduling platform dynamics, thereby improving the response speed and accuracy of cloud computing resource scheduling jobs and improving the quality of cloud computing resource scheduling services.

[0031] Example 2: See also Figure 1 - Figure 2 , a cloud computing resource scheduling system based on artificial intelligence, used to implement a cloud computing resource scheduling method based on artificial intelligence, the system includes a cloud computing platform information collection module, a cloud computing platform analysis module, and a cloud computing resource scheduling module; The cloud computing platform information collection module includes a user-side computing resource demand collection unit, a user-side spatial location collection unit, and an operational cloud computing platform feature information search unit; The user-side computing power resource demand collection unit collects the user-side computing power resource demand data through the cloud computing scheduling platform; the user-side spatial location collection unit collects the user-side spatial location coordinate data through the cloud computing scheduling platform; the operational cloud computing platform feature information search unit searches and processes the operational cloud computing platform feature information based on the Internet platform to generate operational cloud computing platform feature text data; The cloud computing platform analysis module includes an operational cloud computing platform computing power resource margin collection unit, an operational cloud computing platform communication network speed collection unit, an operational cloud computing platform server spatial location collection unit, a cloud computing platform computing power resource analysis unit, a user-side cloud computing communication transmission demand network speed interval storage unit, a cloud computing platform communication network speed evaluation unit, and a cloud computing platform server spatial location analysis unit; The computing power resource surplus collection unit of the operational cloud computing platform collects and processes the computing power resource surplus of the cloud computing platform based on the characteristic text data of the operational cloud computing platform in combination with the cloud computing scheduling platform, and generates the computing power resource surplus data of the operational cloud computing platform; the communication network speed collection unit of the operational cloud computing platform collects and processes the communication network speed of the cloud computing platform based on the characteristic text data of the operational cloud computing platform in combination with the cloud computing scheduling platform, and generates the communication network speed data of the operational cloud computing platform; the spatial position collection unit of the server of the operational cloud computing platform collects and processes the spatial position coordinates of the server of the cloud computing platform based on the characteristic text data of the operational cloud computing platform in combination with the cloud computing scheduling platform, and generates the spatial position coordinate data of the server of the operational cloud computing platform; the computing power resource analysis unit of the cloud computing platform performs cloud computing to meet the computing power resource demand of the user end according to the computing power resource demand data of the user end and the computing power resource surplus data of the operational cloud computing platform. Platform analysis and processing to generate user-side computing power demand cloud computing platform analysis data; user-side cloud computing communication transmission demand network speed interval storage unit for storing user-side cloud computing communication transmission demand network speed interval; cloud computing platform communication network speed evaluation unit, based on user-side computing power demand cloud computing platform analysis data, operational cloud computing platform communication network speed data and user-side cloud computing communication transmission demand network speed interval, performs cloud computing platform evaluation processing to meet user-side computing power resource and communication network speed requirements, and generates user-side computing power network speed demand cloud computing platform evaluation data; cloud computing platform server spatial location analysis unit, based on user-side computing power network speed demand cloud computing platform evaluation data, operational cloud computing platform server spatial location coordinate data and user-side spatial location coordinate data, performs cloud computing platform analysis processing to meet user-side computing power resource, communication network speed and spatial location requirements, and generates user-side computing power network speed location demand cloud computing platform analysis data; The cloud computing resource scheduling module includes a cloud computing resource scheduling parameter construction unit and a cloud computing resource scheduling job execution unit; The cloud computing resource scheduling parameter construction unit constructs cloud computing resource scheduling analysis data based on the user-side computing power resource demand parameters and the characteristic information of the optimal cloud computing platform required by the user-side; the cloud computing resource scheduling job execution unit executes cloud computing resource scheduling jobs based on the cloud computing resource scheduling analysis data and in combination with the cloud computing scheduling platform.

[0032] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cloud computing resource scheduling method based on artificial intelligence, characterized in that: The method comprises the following steps: S1. Collect the computing resource demand data and spatial location coordinate data of the user terminal; S2. Search and process characteristic information of the cloud computing platform in operation status based on the Internet platform to generate characteristic text data of the cloud computing platform in operation status; S3. Collect and process the computing power resource margin, communication network speed, and spatial location coordinates of the cloud computing platform server, and generate operational computing power resource margin data, operational communication network speed data, and operational spatial location coordinates data of the cloud computing platform server. S4. Perform cloud computing platform analysis and processing to meet the computing power resource requirements of the user end, and generate cloud computing platform analysis data on the computing power requirements of the user end; S5. Evaluate the cloud computing platform that meets the computing power and communication network speed requirements of the user end, and generate evaluation data of the cloud computing platform that meets the computing power and communication network speed requirements of the user end; S6. Perform cloud computing platform analysis and processing to meet the user's computing power resources, communication network speed, and spatial location requirements, and generate cloud computing platform analysis data on the user's computing power, network speed, and location requirements; S7. Construct cloud computing resource scheduling analysis data and execute cloud computing resource scheduling operations.

2. The method for scheduling cloud computing resources based on artificial intelligence according to claim 1, characterized in that: Said S1 comprises the following steps: S11. Collect the computing power resource demand parameters of the user-side data to be processed in real time online through the cloud computing scheduling platform, and generate the computing power resource demand data of the user-side ,in The unit is floating point operations per second; The cloud computing scheduling platform collects the geographic spatial coordinate parameters of the cloud computing user end in real time and generates the user end spatial location coordinate data. .

3. The method for scheduling cloud computing resources based on artificial intelligence according to claim 2, characterized in that: The S2 comprises the following steps: S21. Use the Rabin-Karp algorithm to search online on the Internet platform according to the cloud computing platform keywords to obtain the product feature information of all cloud computing platforms currently in operation, and generate a feature text data set of the cloud computing platforms in operation. , ;in Indicates the The characteristic text data of the operational cloud computing platform corresponding to each cloud computing platform, Indicates the maximum number of cloud computing platforms.

4. The method for scheduling cloud computing resources based on artificial intelligence according to claim 3, characterized in that: The S3 includes the following steps: S31, through the cloud computing scheduling platform according to As stated in The corresponding cloud computing platform characteristic information collects the computing power resource margin information of the cloud computing platform in real time online, and generates a data set of computing power resource margin of the operational cloud computing platform ,in Indicates the The computing power resource surplus data of the operational cloud computing platform corresponding to the cloud computing platform, The unit is floating point operations per second; Through the cloud computing scheduling platform according to As stated in The corresponding cloud computing platform characteristic information collects the communication network speed information of the cloud computing platform in real time online, and generates the communication network speed data set of the operational cloud computing platform ,in Indicates the The communication network speed data of the operational cloud computing platform corresponding to the cloud computing platform, The unit is bytes per second; Through the cloud computing scheduling platform according to As stated in The corresponding cloud computing platform feature information collects the geographic spatial location coordinate information of the cloud computing platform server in real time online, and generates the operational cloud computing platform server spatial location coordinate data set ,in Indicates the The spatial location coordinate data of the operational cloud computing platform server corresponding to each cloud computing platform.

5. The method for scheduling cloud computing resources based on artificial intelligence according to claim 4, characterized in that: The S4 comprises the following steps: S41, the With the As stated in Compare the computing power resource values ​​and search for computing power resources with a value not less than the Matching the The corresponding cloud computing platform quantity number text information, and generate the user-side computing power demand cloud computing platform analysis data set , execute and generate the user terminal computing power demand cloud computing platform analysis data set The specific steps are as follows: S411, initialize algorithm parameters, population size N, maximum number of iterations T; S412: Initialize the population, calculate the fitness, and determine the cloud computing platform search pathfinder and the cloud computing platform search follower; S413, proceed as described above Update the cloud computing platform search pathfinder position in the search space; S414, update the cloud computing platform to search for followers in the The position in the search space of S415, calculate the The search space is described in With the The fitness value, and in the Update the search space to find a computing resource value not less than the Matches all of the Global optimal value; S416, when the maximum number of iterations is met, the output of step S415 and the Matching the The corresponding cloud computing platform quantity number text information, and data identification is used to generate the user-side computing power demand cloud computing platform analysis data set , ,in Indicates the The cloud computing platform analyzes the computing power demand of the user end corresponding to each cloud computing platform. Indicates the The cloud computing platform analyzes data on the computing power requirements of the user end corresponding to each cloud computing platform.

6. The method for scheduling cloud computing resources based on artificial intelligence according to claim 5, characterized in that: The S5 comprises the following steps: S51. Establish the network speed range required for user-side cloud computing communication transmission ,in and They represent the minimum network speed data required for cloud computing communication transmission at the user end and the maximum network speed data required for cloud computing communication transmission at the user end, and The unit of data is bytes per second; S52, using iterative deepening search algorithm according to As stated in to The corresponding cloud computing platform quantity number text information is described in Search the search space to find all cloud computing platforms corresponding to the , and will search out all of the With the As stated in and stated Compare the communication network speed values ​​and search for the communication network speed value that belongs to the All of the above The corresponding cloud computing platform quantity number text information, and data identification is used to generate the user-side computing power and network speed demand cloud computing platform evaluation data set , ,in Indicates the Cloud computing platform evaluation data on user-side computing power and network speed requirements corresponding to each cloud computing platform, Indicates the Cloud computing platform evaluation data on user-side computing power and network speed requirements corresponding to each cloud computing platform.

7. The method for scheduling cloud computing resources based on artificial intelligence according to claim 6, characterized in that: The S6 comprises the following steps: S61, using the depth-limited search algorithm according to the As stated in to The corresponding cloud computing platform quantity number text information is described in Search the search space to find all cloud computing platforms corresponding to the , and the searched Respectively with the Combined with the spatial distance formula, the spatial distance between the user end and the cloud computing platform is numerically processed to measure the spatial distance data set between the user end and the cloud computing platform. ,in Indicates the The spatial distance data from the user end to the cloud computing platform corresponding to the cloud computing platform to the user end, Indicates the The spatial distance data from the user end to the cloud computing platform corresponding to the cloud computing platform to the user end, and The unit is meter; S62, using a unified cost search algorithm in the Search for the cloud computing platform quantity number text information corresponding to the spatial distance data from the user terminal to the cloud computing platform with the smallest spatial distance value, and generate the user terminal computing power, network speed and location demand cloud computing platform analysis data through data identification. .

8. The method for scheduling cloud computing resources based on artificial intelligence according to claim 7, characterized in that: The S7 comprises the following steps: S71, the 、 and stated Perform data combination identification to construct cloud computing resource scheduling analysis data ; S72, the cloud computing scheduling platform is as described As stated in The corresponding cloud computing platform quantity number text information, and in the Search the search space for the The corresponding ; S73, the cloud computing scheduling platform searches for the Control the corresponding cloud computing platform as described Execute cloud computing resource scheduling jobs for the user side.

9. An artificial intelligence-based cloud computing resource scheduling system, configured to implement the artificial intelligence-based cloud computing resource scheduling method according to any one of claims 1 to 8, characterized in that: The system includes a cloud computing platform information collection module, a cloud computing platform analysis module, and a cloud computing resource scheduling module.

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