Intelligent scheduling management method and system of multi-source service aggregation platform

Abstract

The application discloses an intelligent scheduling management method and system of a multi-source service aggregation platform, relates to the technical field of platform intelligent scheduling management, and comprises the following steps: obtaining a task set capable of being quickly executed; performing task sorting processing on the task set capable of being quickly executed, and determining a first execution task of the multi-source service aggregation platform. The application firstly obtains a task type conforming to a resource scheduling standard and a task delay coefficient of the task type conforming to the resource scheduling standard, then screens a to-be-executed task set of the multi-source service aggregation platform by using the task type conforming to the resource scheduling standard and the task delay coefficient of the task type conforming to the resource scheduling standard, obtains a task set capable of being quickly executed, and finally performs resource comparison on the task set capable of being quickly executed to obtain the first execution task of the multi-source service aggregation platform, so that the multi-source service aggregation platform is prevented from appearing congested, and the efficiency of processing tasks of the multi-source service aggregation platform is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling and management technology for platforms, specifically to an intelligent scheduling and management method and system for a multi-source service aggregation platform. Background Technology

[0002] A multi-source service aggregation platform is a "platform within a platform." It uses technology to integrate multiple independent systems, services, or resources into a unified interface, enabling one-stop management. It does not directly produce content or provide services; instead, it acts as an "intermediary" or "dispatch center," helping users efficiently connect to and utilize dispersed resources.

[0003] Multi-source service aggregation platforms execute different types of tasks. However, some types of tasks require a lot of time to load resources. If there are too few tasks, it can wait. But when there are too many tasks, it will cause congestion on the multi-source service aggregation platform, thereby affecting the efficiency of the platform in processing tasks. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides an intelligent scheduling and management method and system for a multi-source service aggregation platform. This technical solution resolves the issues raised in the background section.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An intelligent scheduling and management method for a multi-source service aggregation platform, comprising: Acquire historical operational data from the multi-source service aggregation platform, perform type analysis on the historical operational data of the multi-source service aggregation platform, and obtain task types that conform to resource scheduling standards; Obtain successful task data from the multi-source service aggregation platform. Based on task types that meet resource scheduling standards, perform time analysis on the successful task data from the multi-source service aggregation platform to obtain task latency coefficients for task types that meet resource scheduling standards. Obtain the set of tasks to be executed from the multi-source service aggregation platform. Based on the task types that meet the resource scheduling standards and the task latency coefficients of the task types that meet the resource scheduling standards, perform task filtering on the set of tasks to be executed from the multi-source service aggregation platform to obtain a set of tasks that can be executed quickly. The task set that can be executed quickly is sorted to determine the first task to be executed by the multi-source service aggregation platform.

[0006] Preferably, the step of obtaining historical operational data from the multi-source service aggregation platform, performing type analysis on the historical operational data, and obtaining task types that conform to resource scheduling standards specifically includes the following steps: Based on the operational parameter characteristics of the multi-source service aggregation platform, data retrieval processing is performed on the data storage institution of the multi-source service aggregation platform to obtain the historical operational data of the multi-source service aggregation platform; Based on the task number composition method, the historical operation data of the multi-source service aggregation platform is filtered and processed to obtain the historical task operation data of the multi-source service aggregation platform. Intersection processing is performed on the task content of historical task execution data from the multi-source service aggregation platform to obtain historical task execution data of the same type. Perform data calculations and processing on historical task execution data of the same type to obtain the resource scheduling success rate of different types of tasks; The success rate of resource scheduling for different types of tasks is compared and analyzed to identify the task types that meet the resource scheduling criteria.

[0007] Preferably, the step of performing data calculations on historical task execution data of the same type to obtain the resource scheduling success rate of different types of tasks specifically includes the following steps: Classify and count historical task execution data of the same type based on successful resource scheduling, and obtain the number of tasks with the same type of resources that were successfully scheduled. Count the historical task execution data of the same type to obtain the total number of historical tasks of the same type. The total number of historically executed tasks of the same type and the number of tasks successfully scheduled using the same type of resources are calculated and processed to obtain the resource scheduling success rate for different types of tasks.

[0008] Preferably, the step of performing data comparison and analysis on the resource scheduling success rates of different types of tasks to obtain the task types that meet the resource scheduling criteria specifically includes the following steps: The system assesses and processes the resource scheduling success rate for different types of tasks and sets a threshold for the resource scheduling success rate. If there are data in the resource scheduling success rate of different types of tasks that are greater than or equal to the set resource scheduling success rate threshold; Set the task type corresponding to data that is greater than or equal to the set resource scheduling success rate threshold to a task type that conforms to the resource scheduling standard. Set the task type corresponding to data that is less than the set resource scheduling success rate threshold to a task type that does not meet the resource scheduling standard.

[0009] Preferably, the step of obtaining the successfully executed task data of the multi-source service aggregation platform, based on the task type that conforms to the resource scheduling standard, and performing time analysis processing on the successfully executed task data of the multi-source service aggregation platform to obtain the task latency coefficient of the task type that conforms to the resource scheduling standard, specifically includes the following steps: The historical operational data of the multi-source service aggregation platform is filtered and processed based on the feature of successful resource scheduling to obtain the data of successfully executed tasks of the multi-source service aggregation platform. Based on the task types that meet the resource scheduling standards, a secondary data filtering process is performed on the successful task data of the multi-source service aggregation platform to obtain successful task data that meets the resource scheduling standards. Data processing is performed on successfully executed task data that meets resource scheduling standards to obtain the task delay coefficient for task types that meet the resource scheduling standards.

[0010] Preferably, the step of performing data calculation and processing on the successfully executed task data that conforms to the resource scheduling criteria to obtain the task delay coefficient of the task type that conforms to the resource scheduling criteria specifically includes the following steps: Extract and process information from the data of successfully executed tasks that meet the resource scheduling standards to obtain the task arrival time, resource scheduling completion time, and task start time of completed tasks. The time spent on resource scheduling is obtained by performing a difference calculation on the task arrival time and resource scheduling completion time. The difference between the resource scheduling completion time and the task start time of completed tasks is calculated to obtain the resource loading time. Set the ratio between the time spent on resource scheduling and the time spent on resource loading as the task latency coefficient for task types that conform to resource scheduling standards.

[0011] Preferably, the step of obtaining the set of tasks to be executed from the multi-source service aggregation platform, based on the task types that conform to resource scheduling standards and the task latency coefficients of the task types that conform to resource scheduling standards, and performing task filtering processing on the set of tasks to be executed from the multi-source service aggregation platform to obtain a set of tasks that can be executed quickly, specifically includes the following steps: Perform data retrieval processing on the task manager of the multi-source service aggregation platform to obtain the set of tasks to be executed on the multi-source service aggregation platform; Based on the task types that meet the resource scheduling standards, the set of tasks to be executed on the multi-source service aggregation platform is subjected to the first screening process to obtain the set of tasks to be executed that meet the resource scheduling standards. The task delay coefficient and the set task delay coefficient threshold are judged and processed for task types that meet the resource scheduling standards; If there are data in the task delay coefficient of a task type that meets the resource scheduling standard that are greater than or equal to the set task delay coefficient threshold, set the task type corresponding to the data that are greater than or equal to the set task delay coefficient threshold as the resource fast adaptation task type. Based on the resource-based rapid adaptation to task types, a second screening process is performed on the set of tasks to be executed that meet the resource scheduling criteria to obtain a set of tasks that can be executed quickly.

[0012] Preferably, the step of sorting the set of tasks that can be executed quickly to determine the first task to be executed by the multi-source service aggregation platform specifically includes the following steps: Based on the priority task coding method, tasks in the task set that can be executed quickly are filtered to obtain the priority task set; For each task in the priority task set, perform data reading and processing to obtain the generation time of each priority task; Based on the generation time of each priority task, the priority task set is sorted to obtain an ordered set of priority tasks; Obtain the remaining available resources of the multi-source service aggregation platform, compare the data of the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform, and determine the first task to be executed by the multi-source service aggregation platform.

[0013] Preferably, the step of obtaining the remaining available resources of the multi-source service aggregation platform, comparing the ordered set of priority tasks with the remaining available resources of the multi-source service aggregation platform, and determining the task to be executed first on the multi-source service aggregation platform specifically includes the following steps: Perform data retrieval processing on the resource manager of the multi-source service aggregation platform to obtain the remaining available resources of the multi-source service aggregation platform; The system determines and processes the resources required for the first task in the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform. If the resources required by the first task in the priority task ordered set are less than the remaining available resources of the multi-source service aggregation platform, the first task in the priority task ordered set will be set as the first task to be executed by the multi-source service aggregation platform. If the resources required by the first task in the priority task ordered set are greater than or equal to the remaining available resources of the multi-source service aggregation platform, the first task in the priority task ordered set cannot be executed. The resources required by the second task in the priority task ordered set are compared with the remaining available resources of the multi-source service aggregation platform until a task with less resources than the remaining available resources of the multi-source service aggregation platform is found. The task with less resources than the remaining available resources of the multi-source service aggregation platform is set as the first task to be executed by the multi-source service aggregation platform.

[0014] Furthermore, an intelligent scheduling and management system for a multi-source service aggregation platform is proposed to implement the intelligent scheduling and management method for a multi-source service aggregation platform as described above, including: The resource scheduling terminal is used to control the data transmission and information interaction between various modules. The resource scheduling terminal is used to control each module to perform task filtering on the set of tasks to be executed by the multi-source service aggregation platform through the historical operation data of the multi-source service aggregation platform, and to determine the first task to be executed by the multi-source service aggregation platform. The resource scheduling and analysis module is used to classify tasks and compare resource scheduling success rates based on the historical operation data of the multi-source service aggregation platform, and to obtain task types that meet the resource scheduling standards. The task delay coefficient calculation module is used to perform data calculation and processing on the successful execution data of the multi-source service aggregation platform to determine the task delay coefficient of the task type that meets the resource scheduling standard. The first task filtering module performs multiple task filtering processes on the set of tasks to be executed on the multi-source service aggregation platform based on the task types that meet the resource scheduling standards and the task delay coefficients of the task types that meet the resource scheduling standards, in order to obtain a set of tasks that can be executed quickly. The second task filtering module is used to perform task priority analysis and resource comparison on the set of tasks that can be executed quickly, and to determine the first task to be executed by the multi-source service aggregation platform.

[0015] Compared with existing technologies, the present invention provides an intelligent scheduling and management method and system for a multi-source service aggregation platform, which has the following beneficial effects: This invention first performs type analysis and task resource loading time analysis on the historical operating data of the multi-source service aggregation platform to obtain task types that meet resource scheduling standards and task latency coefficients for those task types. Then, it filters the set of tasks to be executed on the multi-source service aggregation platform using these task types and their latency coefficients to obtain a set of tasks that can be executed quickly. Finally, it compares the resources of the set of tasks that can be executed quickly to determine the tasks that will be executed first on the multi-source service aggregation platform. This ensures that the multi-source service aggregation platform will not experience congestion and improves the efficiency of task processing. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating steps S100-S400 in the intelligent scheduling and management method for a multi-source service aggregation platform proposed in this invention. Figure 2 This is a structural block diagram of an intelligent scheduling and management system for a multi-source service aggregation platform proposed in this invention. Detailed Implementation

[0017] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0018] Reference Figure 1 As shown, an intelligent scheduling and management method for a multi-source service aggregation platform includes: S100. Obtain historical operation data from the multi-source service aggregation platform, perform type analysis on the historical operation data of the multi-source service aggregation platform, and obtain task types that conform to resource scheduling standards. S200. Obtain the execution success data of the multi-source service aggregation platform. Based on the task type that conforms to the resource scheduling standard, perform time analysis processing on the execution success data of the multi-source service aggregation platform to obtain the task delay coefficient of the task type that conforms to the resource scheduling standard. S300: Obtain the set of tasks to be executed from the multi-source service aggregation platform. Based on the task types that meet the resource scheduling standards and the task delay coefficients of the task types that meet the resource scheduling standards, perform task filtering on the set of tasks to be executed from the multi-source service aggregation platform to obtain a set of tasks that can be executed quickly. S400: Perform task sorting on the set of tasks that can be executed quickly, and determine the first task to be executed by the multi-source service aggregation platform; Those skilled in the art will understand that multi-source service aggregation platforms handle different types of tasks. Some tasks load resources quickly and can be executed soon, while others load resources slowly and require a lot of time. If the multi-source service aggregation platform doesn't have many tasks, it can wait for resources to load. However, if the platform has many tasks, it will become congested, which will reduce the platform's task processing speed. Therefore, by performing feature analysis on the historical operating data of the multi-source service aggregation platform to identify the types of tasks that load resources slowly, and temporarily suspending the execution of tasks when there are too many, tasks that load resources quickly can be executed first. This can improve the task processing speed of the multi-source service aggregation platform without causing congestion. Example 1

[0019] Step S100: Obtain historical operation data from the multi-source service aggregation platform, perform type analysis on the historical operation data of the multi-source service aggregation platform, and obtain task types that meet the resource scheduling standards. This specifically includes the following steps: S101. Based on the operating parameter characteristics of the multi-source service aggregation platform, perform data retrieval processing on the data storage institution of the multi-source service aggregation platform to obtain the historical operating data of the multi-source service aggregation platform; S102. Based on the task number composition method, perform data filtering and processing on the historical operation data of the multi-source service aggregation platform to obtain the historical task operation data of the multi-source service aggregation platform. S103. Perform intersection processing on the task content of the historical task operation data of the multi-source service aggregation platform to obtain historical task operation data of the same type. S104. Perform data calculation and processing on the historical task execution data of the same type to obtain the resource scheduling success rate of different types of tasks; S105. Perform data comparison and analysis on the resource scheduling success rate of different types of tasks to obtain the task types that meet the resource scheduling standards. Specifically, step S104, which involves processing historical task execution data of the same type to obtain the resource scheduling success rate of different task types, includes the following steps: S1041. Classify and count the historical task execution data of the same type based on the feature of successful resource scheduling, and obtain the number of tasks with the same type of resources that were successfully scheduled. S1042. Count the historical task execution data of the same type and obtain the total number of historical tasks of the same type. S1043. Perform data calculation and processing on the total number of historically executed tasks of the same type and the number of tasks successfully scheduled with the same type of resources to obtain the resource scheduling success rate of different types of tasks. It is understandable that the success rate of resource scheduling varies for different types of tasks. Some types of tasks cannot connect to resources or repeatedly disconnect during the connection process. These types of tasks will affect the speed at which the multi-source service aggregation platform processes tasks. Therefore, the success rate of resource scheduling for each type of task is calculated to obtain the success rate of resource scheduling for different types of tasks. Step S105, which involves comparing and analyzing the resource scheduling success rates of different task types to obtain task types that meet the resource scheduling criteria, specifically includes the following steps: S1051. Judge and process the resource scheduling success rate and set resource scheduling success rate threshold for different types of tasks. S1052. If there are data in the resource scheduling success rate of different types of tasks that are greater than or equal to the set resource scheduling success rate threshold. S1053. Set the task type corresponding to data that is greater than or equal to the set resource scheduling success rate threshold to a task type that conforms to the resource scheduling standard. S1054. Set the task type corresponding to data that is less than the set resource scheduling success rate threshold to a task type that does not meet the resource scheduling standard. In this embodiment, in order to improve the processing speed of the multi-source service aggregation platform, it is necessary to remove task types with low resource scheduling success rates. If these tasks are not removed, the multi-source service aggregation platform will become congested when there are too many tasks. Therefore, these types of tasks can only be processed when there are few tasks on the multi-source service aggregation platform. When there are too many tasks on the multi-source service aggregation platform, they need to be filtered out and processed again when the multi-source service aggregation platform is idle. Example 2

[0020] Step S200: Obtain the successful task data from the multi-source service aggregation platform. Based on the task types that conform to the resource scheduling standards, perform time analysis processing on the successful task data from the multi-source service aggregation platform to obtain the task latency coefficients for task types that conform to the resource scheduling standards. This specifically includes the following steps: S201. Based on the successful resource scheduling, perform data filtering and processing on the historical operation data of the multi-source service aggregation platform to obtain the successful execution task data of the multi-source service aggregation platform. S202. Based on the task types that meet the resource scheduling standards, perform secondary data filtering on the successful execution task data of the multi-source service aggregation platform to obtain successful execution task data that meet the resource scheduling standards. S203. Perform data calculation and processing on the successfully executed task data that meets the resource scheduling standard to obtain the task delay coefficient of the task type that meets the resource scheduling standard. Specifically, step S203, which involves processing the data of successfully executed tasks that meet the resource scheduling criteria to obtain the task latency coefficient for task types that meet the resource scheduling criteria, includes the following steps: S2031. Extract and process information from the data of successfully executed tasks that meet the resource scheduling standards to obtain the task arrival time, resource scheduling completion time, and task start time of completed tasks. S2032. Perform data difference calculation on the task arrival time and resource scheduling completion time to obtain the resource scheduling time. S2033. Perform data difference calculation on the resource scheduling completion time and the task start time of completed tasks to obtain the resource loading time; S2034. Set the ratio between resource scheduling time and resource loading time as the task latency coefficient for task types that conform to resource scheduling standards. In this embodiment, some types of tasks load resources slowly. These tasks can be processed when the multi-source service aggregation platform is idle, but cannot be processed when the multi-source service aggregation platform has many tasks. Therefore, the ratio between resource scheduling time and resource loading time is judged. The larger the ratio, the shorter the resource loading time; the smaller the ratio, the longer the resource loading time. These types of tasks cannot be processed when the multi-source service aggregation platform has many tasks. Therefore, the tasks to be executed can be filtered by the task latency coefficient of the task type in the resource scheduling standard to determine the tasks that can load resources quickly and process them first. Example 3

[0021] Step S300: Obtain the set of tasks to be executed from the multi-source service aggregation platform. Based on the task types that meet the resource scheduling standards and the task latency coefficients of the task types that meet the resource scheduling standards, perform task filtering processing on the set of tasks to be executed from the multi-source service aggregation platform to obtain a set of tasks that can be executed quickly. This specifically includes the following steps: S301. Perform data retrieval processing on the task manager of the multi-source service aggregation platform to obtain the set of tasks to be executed on the multi-source service aggregation platform; S302. Based on the task types that meet the resource scheduling standards, perform the first screening process on the set of tasks to be executed on the multi-source service aggregation platform to obtain a set of tasks to be executed that meet the resource scheduling standards. S303. Judge and process the task delay coefficient and the set task delay coefficient threshold for task types that meet the resource scheduling standards; S304. If there are data in the task delay coefficient of a task type that meets the resource scheduling standard that are greater than or equal to the set task delay coefficient threshold, set the task type corresponding to the data that are greater than or equal to the set task delay coefficient threshold as the resource fast adaptation task type. S305. Based on resource-fast task type adaptation, perform a second screening process on the set of tasks to be executed that meet the resource scheduling standards to obtain a set of tasks that can be executed quickly. In this embodiment, the set of tasks to be executed by the multi-source service aggregation platform is filtered by the task type that meets the resource scheduling standard and the task delay coefficient of the task type that meets the resource scheduling standard. This not only filters out task types with low resource scheduling success rate, but also removes task types with excessively long resource loading time. This allows the multi-source service aggregation platform to prioritize the execution of tasks with high resource scheduling success rate and short resource loading time, thereby improving the speed of task processing of the multi-source service aggregation platform. Example 4

[0022] Step S400: Sort the tasks that can be executed quickly to determine the first task to be executed by the multi-source service aggregation platform. This includes the following steps: S401. Based on the priority task coding method, the tasks in the task set that can be executed quickly are filtered to obtain the priority task set. S402. Perform data reading and processing on each task in the priority task set to obtain the generation time of each priority task; S403. Based on the generation time of each priority task, sort the priority task set to obtain an ordered set of priority tasks. S404. Obtain the remaining available resources of the multi-source service aggregation platform, compare the data of the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform, and determine the first task to be executed by the multi-source service aggregation platform. Specifically, step S404, obtaining the remaining available resources of the multi-source service aggregation platform, and performing data comparison processing on the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform to determine the first task to be executed on the multi-source service aggregation platform, includes the following steps: S4041. Perform data retrieval processing on the resource manager of the multi-source service aggregation platform to obtain the remaining available resources of the multi-source service aggregation platform; S4042. Determine and process the resources required by the first task in the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform. S4043. If the resources required by the first task in the priority task ordered set are less than the remaining available resources of the multi-source service aggregation platform, the first task in the priority task ordered set shall be set as the first task to be executed by the multi-source service aggregation platform. S4044. If the resources required by the first task in the priority task ordered set are greater than or equal to the remaining available resources of the multi-source service aggregation platform, the first task in the priority task ordered set cannot be executed. The resources required by the second task in the priority task ordered set are compared with the remaining available resources of the multi-source service aggregation platform until a task with less resources than the remaining available resources of the multi-source service aggregation platform is found. The task with less resources than the remaining available resources of the multi-source service aggregation platform is set as the first task to be executed by the multi-source service aggregation platform. In this embodiment, once the set of tasks that can be executed quickly is determined, it is necessary to sort the tasks in the set and determine the tasks to be executed first. Therefore, the set of priority tasks is filtered by the generation time of each priority task and the remaining available resources of the multi-source service aggregation platform to prevent the multi-source service aggregation platform from becoming overloaded and to ensure the normal operation of the multi-source service aggregation platform.

[0023] Reference Figure 2 As shown, an intelligent scheduling and management system for a multi-source service aggregation platform is used to implement the intelligent scheduling and management method for a multi-source service aggregation platform as described above, including: The resource scheduling terminal is used to control the data transmission and information interaction between various modules. The resource scheduling terminal is used to control each module to perform task filtering on the set of tasks to be executed by the multi-source service aggregation platform through the historical operation data of the multi-source service aggregation platform, and to determine the first task to be executed by the multi-source service aggregation platform. The resource scheduling and analysis module is used to classify tasks and compare resource scheduling success rates based on the historical operation data of the multi-source service aggregation platform, and to obtain task types that meet the resource scheduling standards. The task delay coefficient calculation module is used to perform data calculation and processing on the successful execution data of the multi-source service aggregation platform to determine the task delay coefficient of the task type that meets the resource scheduling standard. The first task filtering module performs multiple task filtering processes on the set of tasks to be executed on the multi-source service aggregation platform based on the task types that meet the resource scheduling standards and the task delay coefficients of the task types that meet the resource scheduling standards, in order to obtain a set of tasks that can be executed quickly. The second task filtering module is used to perform task priority analysis and resource comparison on the set of tasks that can be executed quickly, and to determine the first task to be executed by the multi-source service aggregation platform.

[0024] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An intelligent scheduling and management method for a multi-source service aggregation platform, characterized in that, include: Acquire historical operational data from the multi-source service aggregation platform, perform type analysis on the historical operational data of the multi-source service aggregation platform, and obtain task types that conform to resource scheduling standards; Obtain successful task data from the multi-source service aggregation platform. Based on task types that meet resource scheduling standards, perform time analysis on the successful task data from the multi-source service aggregation platform to obtain task latency coefficients for task types that meet resource scheduling standards. Obtain the set of tasks to be executed from the multi-source service aggregation platform. Based on the task types that meet the resource scheduling standards and the task latency coefficients of the task types that meet the resource scheduling standards, perform task filtering on the set of tasks to be executed from the multi-source service aggregation platform to obtain a set of tasks that can be executed quickly. The task set that can be executed quickly is sorted to determine the first task to be executed by the multi-source service aggregation platform.

2. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 1, characterized in that, The process of acquiring historical operational data from the multi-source service aggregation platform, performing type analysis on the historical operational data, and obtaining task types that conform to resource scheduling standards specifically includes the following steps: Based on the operational parameter characteristics of the multi-source service aggregation platform, data retrieval processing is performed on the data storage institution of the multi-source service aggregation platform to obtain the historical operational data of the multi-source service aggregation platform; Based on the task number composition method, the historical operation data of the multi-source service aggregation platform is filtered and processed to obtain the historical task operation data of the multi-source service aggregation platform. Intersection processing is performed on the task content of historical task execution data from the multi-source service aggregation platform to obtain historical task execution data of the same type. Perform data calculations and processing on historical task execution data of the same type to obtain the resource scheduling success rate of different types of tasks; The success rate of resource scheduling for different types of tasks is compared and analyzed to identify the task types that meet the resource scheduling criteria.

3. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 2, characterized in that, The process of performing data calculations on historical task execution data of the same type to obtain the resource scheduling success rate of different task types specifically includes the following steps: Classify and count historical task execution data of the same type based on successful resource scheduling, and obtain the number of tasks with the same type of resources that were successfully scheduled. Count the historical task execution data of the same type to obtain the total number of historical tasks of the same type. The total number of historically executed tasks of the same type and the number of tasks successfully scheduled using the same type of resources are calculated and processed to obtain the resource scheduling success rate for different types of tasks.

4. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 3, characterized in that, The process of comparing and analyzing the resource scheduling success rates of different task types to obtain the task types that meet the resource scheduling criteria includes the following steps: The system assesses and processes the resource scheduling success rate for different types of tasks and sets a threshold for the resource scheduling success rate. If there are data in the resource scheduling success rate of different types of tasks that are greater than or equal to the set resource scheduling success rate threshold; Set the task type corresponding to data that is greater than or equal to the set resource scheduling success rate threshold to a task type that conforms to the resource scheduling standard. Set the task type corresponding to data that is less than the set resource scheduling success rate threshold to a task type that does not meet the resource scheduling standard.

5. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 4, characterized in that, The process of obtaining successful task data from the multi-source service aggregation platform, based on task types that conform to resource scheduling standards, and performing time analysis on the successful task data to obtain task latency coefficients for task types that conform to resource scheduling standards, specifically includes the following steps: The historical operational data of the multi-source service aggregation platform is filtered and processed based on the feature of successful resource scheduling to obtain the data of successfully executed tasks of the multi-source service aggregation platform. Based on the task types that meet the resource scheduling standards, a secondary data filtering process is performed on the successful task data of the multi-source service aggregation platform to obtain successful task data that meets the resource scheduling standards. Data processing is performed on successfully executed task data that meets resource scheduling standards to obtain the task delay coefficient for task types that meet the resource scheduling standards.

6. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 5, characterized in that, The process of calculating and processing data on successfully executed tasks that meet resource scheduling standards to obtain the task latency coefficient for task types that meet resource scheduling standards specifically includes the following steps: Extract and process information from the data of successfully executed tasks that meet the resource scheduling standards to obtain the task arrival time, resource scheduling completion time, and task start time of completed tasks. The time spent on resource scheduling is obtained by performing a difference calculation on the task arrival time and resource scheduling completion time. The difference between the resource scheduling completion time and the task start time of completed tasks is calculated to obtain the resource loading time. Set the ratio between the time spent on resource scheduling and the time spent on resource loading as the task latency coefficient for task types that conform to resource scheduling standards.

7. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 6, characterized in that, The process of obtaining the set of tasks to be executed from the multi-source service aggregation platform, based on the task types and their latency coefficients that conform to resource scheduling standards, and then filtering the set of tasks to be executed from the multi-source service aggregation platform to obtain a set of tasks that can be executed quickly, specifically includes the following steps: Perform data retrieval processing on the task manager of the multi-source service aggregation platform to obtain the set of tasks to be executed on the multi-source service aggregation platform; Based on the task types that meet the resource scheduling standards, the set of tasks to be executed on the multi-source service aggregation platform is subjected to the first screening process to obtain the set of tasks to be executed that meet the resource scheduling standards. The task delay coefficient and the set task delay coefficient threshold are judged and processed for task types that meet the resource scheduling standards; If there are data in the task delay coefficient of a task type that meets the resource scheduling standard that are greater than or equal to the set task delay coefficient threshold, set the task type corresponding to the data that are greater than or equal to the set task delay coefficient threshold as the resource fast adaptation task type. Based on the resource-based rapid adaptation to task types, a second screening process is performed on the set of tasks to be executed that meet the resource scheduling criteria to obtain a set of tasks that can be executed quickly.

8. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 7, characterized in that, The process of sorting the set of tasks that can be executed quickly to determine the first task to be executed by the multi-source service aggregation platform specifically includes the following steps: Based on the priority task coding method, tasks in the task set that can be executed quickly are filtered to obtain the priority task set; For each task in the priority task set, perform data reading and processing to obtain the generation time of each priority task; Based on the generation time of each priority task, the priority task set is sorted to obtain an ordered set of priority tasks; Obtain the remaining available resources of the multi-source service aggregation platform, compare the data of the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform, and determine the first task to be executed by the multi-source service aggregation platform.

9. The intelligent scheduling and management method for a multi-source service aggregation platform according to claim 8, characterized in that, The process of obtaining the remaining available resources of the multi-source service aggregation platform, comparing the ordered set of priority tasks with the remaining available resources of the multi-source service aggregation platform, and determining the task to be executed first on the multi-source service aggregation platform specifically includes the following steps: Perform data retrieval processing on the resource manager of the multi-source service aggregation platform to obtain the remaining available resources of the multi-source service aggregation platform; The system determines and processes the resources required for the first task in the ordered set of priority tasks and the remaining available resources of the multi-source service aggregation platform. If the resources required by the first task in the priority task ordered set are less than the remaining available resources of the multi-source service aggregation platform, the first task in the priority task ordered set will be set as the first task to be executed by the multi-source service aggregation platform. If the resources required by the first task in the priority task ordered set are greater than or equal to the remaining available resources of the multi-source service aggregation platform, the first task in the priority task ordered set cannot be executed. The resources required by the second task in the priority task ordered set are compared with the remaining available resources of the multi-source service aggregation platform until a task with less resources than the remaining available resources of the multi-source service aggregation platform is found. The task with less resources than the remaining available resources of the multi-source service aggregation platform is set as the first task to be executed by the multi-source service aggregation platform.

10. An intelligent scheduling and management system for a multi-source service aggregation platform, used to implement the intelligent scheduling and management method for a multi-source service aggregation platform as described in any one of claims 1-9, characterized in that, include: The resource scheduling terminal is used to control the data transmission and information interaction between various modules. The resource scheduling terminal is used to control each module to perform task filtering on the set of tasks to be executed by the multi-source service aggregation platform through the historical operation data of the multi-source service aggregation platform, and to determine the first task to be executed by the multi-source service aggregation platform. The resource scheduling and analysis module is used to classify tasks and compare resource scheduling success rates based on the historical operation data of the multi-source service aggregation platform, and to obtain task types that meet the resource scheduling standards. The task delay coefficient calculation module is used to perform data calculation and processing on the successful execution data of the multi-source service aggregation platform to determine the task delay coefficient of the task type that meets the resource scheduling standard. The first task filtering module performs multiple task filtering processes on the set of tasks to be executed on the multi-source service aggregation platform based on the task types that meet the resource scheduling standards and the task delay coefficients of the task types that meet the resource scheduling standards, in order to obtain a set of tasks that can be executed quickly. The second task filtering module is used to perform task priority analysis and resource comparison on the set of tasks that can be executed quickly, and to determine the first task to be executed by the multi-source service aggregation platform.

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