Computer intelligent service management system and method based on network big data

Abstract

The invention relates to the technical field of intelligent management, in particular to a computer intelligent service management system and method based on network big data, and the system comprises a service state monitoring module, an operation state modeling module, a service optimization module, an execution scheme adjustment module and a service task distribution module. According to the invention, through data dynamic analysis of the resource consumption proportion, the task processing duration and the completion time, reliable support is provided for task allocation, the resource occupation relationship between tasks is disclosed, the rationality and efficiency improvement of resource allocation are ensured, the task allocation deviation and resource allocation are optimized, and the resource use is dynamically balanced; the service execution efficiency and accuracy are improved, resource waste and task delay are reduced by analyzing task delay and resource utilization changes, identifying execution bottlenecks and adjusting structures, the execution sequence is dynamically optimized in combination with task completion time and a resource allocation proportion, and the system operation efficiency and response capability are improved. And meanwhile, resource utilization, task scheduling and service quality are optimized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to a computer intelligent service management system and method based on network big data. Background Art

[0002] The field of intelligent management technology includes technologies that optimize the management of resources, services, equipment and processes through modern information technology means, such as big data, artificial intelligence, and the Internet of Things. Its core content is to improve the automation, precision and efficiency of management through data collection, analysis and intelligent decision-making. The field of intelligent management includes application scenarios such as smart city management, intelligent manufacturing, intelligent logistics, and intelligent services. It aims to achieve data-driven efficient management and promote the intelligence, networking and automation of various services and systems. The technical means in this field involve data processing, machine learning, decision support systems, and intelligent optimization algorithms, covering the entire process from data collection, transmission to analysis and processing. The goal is to improve the overall operational efficiency of the system through efficient resource allocation and optimized scheduling.

[0003] Among them, the computer intelligent service management system refers to a system that uses computer technology and artificial intelligence to intelligently control and optimize the information flow and resource flow in the service management process. The patent subject conducts research and innovation on technical matters such as intelligent scheduling, task allocation, and service quality monitoring in service management. The patent proposes to collect and process data, analyze service demand and resource supply in real time, automatically generate service plans, and dynamically adjust according to actual conditions to achieve the optimization of service management. Specifically solve problems such as task allocation, progress tracking, and resource scheduling in the service process, use data mining and intelligent decision-making methods to optimize the service process, ensure the rational allocation of resources, and continuously improve service quality.

[0004] Existing technologies rely on static or preset rules in resource allocation and task scheduling, which are difficult to adapt to complex and dynamic changes in service demand, resulting in frequent problems of unbalanced resource allocation. Due to the lack of fine-grained analysis of resource usage and real-time dynamic task execution, resource waste and task backlogs are relatively common, affecting the efficiency of system operation. Existing technologies use simple rules or fixed logic to match task priorities and resource requirements, which is difficult to flexibly respond to changes in resource supply and demand, resulting in the efficiency and accuracy of task scheduling being restricted. When dealing with task delays and changes in resource utilization, there is a lack of dynamic adjustment mechanisms based on actual data, and delay problems cannot be solved in a timely manner, affecting service response speed. Existing systems lack intelligent analysis of task completion time and resource allocation changes in the planning of task execution sequence, resulting in difficulty in optimizing service solutions, and difficulty in ensuring overall resource utilization and service quality. This limits the service management capabilities of existing technologies in highly dynamic environments and cannot fully meet modern intelligent management needs. Summary of the invention

[0005] In order to solve the problem that the existing technology relies on static or preset rules in resource allocation and task scheduling, it is difficult to adapt to complex and dynamic changes in service requirements, resulting in frequent imbalanced resource allocation. Due to the lack of fine-grained analysis of real-time dynamic resource usage and task execution, resource waste and task backlog are relatively common, affecting the efficiency of system operation. The existing technology uses simple rules or fixed logic to match task priorities and resource requirements, which is difficult to flexibly respond to changes in resource supply and demand, resulting in the efficiency and accuracy of task scheduling being restricted. When dealing with task delays and resource utilization changes, there is a lack of dynamic adjustment mechanisms based on actual data, and the delay problem cannot be solved in time, affecting the service response speed. In the planning of task execution sequence, the existing system lacks intelligent analysis of task completion time and resource allocation changes, resulting in difficulty in optimizing service solutions, overall resource utilization and service quality. It is difficult to guarantee the service management capabilities of the existing technology in a highly dynamic environment and cannot fully meet the technical problems of modern intelligent management needs. The embodiment of the present invention provides a computer intelligent service management system and method based on network big data. The technical solution is as follows: On the one hand, a computer intelligent service management system based on network big data is provided, the system comprising: The service status monitoring module is based on the resource usage and task load data in the network service. By identifying the resource consumption ratio and task processing time, combined with the task completion time, it analyzes the resource allocation and load change trends, and obtains the task resource allocation trend data; The operation status modeling module extracts the task load and resource utilization data based on the task resource allocation trend data, analyzes the resource occupation relationship between tasks, identifies the corresponding association between tasks and resources in combination with the load change trend, and builds a service operation stage data model; The service optimization module extracts the task load deviation value and resource allocation adjustment data based on the service operation phase data model, compares the task priority and resource demand, and obtains the optimized resource task allocation result through the adjustment of task allocation deviation and resource allocation; The execution scheme adjustment module analyzes the impact of task execution adjustment on resource usage based on the optimized resource task allocation result, extracts task delay change and resource utilization change data, corrects the task execution structure, and obtains task execution and resource allocation adjustment data; The service task allocation module adjusts the task execution order according to the task priority and resource allocation based on the task execution and resource allocation adjustment data, extracts the task completion time and resource allocation ratio change data, and constructs an intelligent service task allocation plan.

[0006] As a further solution of the present invention, the task resource allocation trend data includes resource consumption ratio, task processing time and task completion time, the service operation stage data model includes task load data, resource utilization data and the correspondence between tasks and resources, the optimized resource task allocation result includes task load deviation value, resource allocation adjustment data and task priority and resource demand matching relationship, the task execution and resource allocation adjustment data includes task delay change data, resource utilization change data and task execution structure adjustment data, and the intelligent service task allocation plan includes task completion time, resource allocation ratio change data and task execution order.

[0007] As a further solution of the present invention, the service status monitoring module includes: The resource consumption monitoring submodule captures the usage data of CPU, memory, and network bandwidth resources based on the resource usage and task load data in the network service, and performs real-time statistics on resource usage by time period to obtain resource consumption trend data; The load analysis submodule analyzes the load conditions of differentiated tasks within a cycle based on the resource consumption trend data, through the processing time and load data of the tasks, extracts the load fluctuation data, analyzes the time distribution of the task load, and obtains the load change trend data; The task completion trend analysis submodule analyzes the relationship between task completion time and task load based on the load change trend data, identifies the changes in resource requirements during task execution in combination with the resource consumption trend, and obtains task resource allocation trend data.

[0008] As a further solution of the present invention, the operating status modeling module includes: The resource association analysis submodule extracts the resource occupancy during the execution of each task based on the task resource allocation trend data, monitors the resource demand and usage of each task, and performs time series analysis on resource allocation to obtain a resource occupancy relationship data set; The load matching analysis submodule analyzes the load fluctuation of the task during execution based on the resource occupancy relationship data set and the load data of the task, analyzes the matching degree between the task load and resource consumption, identifies the relationship between resource demand and load distribution, and obtains the load and resource matching result; The data model building submodule analyzes the mutual influence between resources and load based on the load and resource matching results, combined with the resource allocation and load changes of the task, and obtains the service operation stage data model.

[0009] As a further solution of the present invention, the service optimization module includes: The task load analysis submodule extracts the load deviation value of the task based on the service operation phase data model, analyzes the fluctuation range of the load during the execution of the task, analyzes the frequency of abnormal load fluctuation, identifies the situation of uneven load distribution, and obtains the task load deviation value; The resource demand matching submodule compares the task priority and the corresponding resource consumption based on the task load deviation value, combines the task priority and the resource demand data, identifies the imbalance of resource allocation, and obtains the task priority and resource demand data; The task resource optimization submodule adjusts the task resource allocation based on the task priority and resource demand data, compares the matching degree between tasks and resources, calculates the difference value of task resource allocation, optimizes resource configuration, and obtains the optimized resource task allocation result.

[0010] As a further solution of the present invention, the difference value of the task resource allocation is calculated according to the formula: ; Where D represents the difference value of task resource allocation, represents the weight coefficient of the i-th task, represents the priority of the i-th task, represents the demand value of the i-th resource, and n represents the total number of tasks.

[0011] As a further solution of the present invention, the execution scheme adjustment module includes: The task delay analysis submodule extracts the task delay data based on the optimized resource task allocation result, analyzes the delay fluctuation range of each task, monitors the delay change during task execution, identifies the deviation of the delay time, and obtains the task delay change result; The resource utilization change submodule collects resource utilization data during task execution based on the task delay change result, analyzes resource utilization changes before and after task execution, identifies the change trend of resource consumption, and obtains a resource utilization change data set; The execution structure correction submodule adjusts the task execution structure based on the resource utilization change data set, corrects the resource allocation and execution order between tasks, calculates the optimized task execution structure adjustment value, and obtains task execution and resource allocation adjustment data.

[0012] As a further solution of the present invention, the optimized task execution structure adjustment value is calculated according to the formula: ; Among them, Q represents the optimized task execution structure adjustment value, represents the task weight coefficient, Represents resource availability, Represents resource utilization efficiency, Represents the task execution time, represents the order weight between tasks, Represents the scheduled task execution time, is the adjustment factor, The flexibility factor is used to adjust the order of task execution.

[0013] As a further solution of the present invention, the service task allocation module includes: The task sequence adjustment submodule adjusts the execution sequence of tasks based on the task execution and resource allocation adjustment data, evaluates the resource requirements and available resources of the tasks, analyzes the impact of resource allocation during task execution, optimizes the execution sequence of tasks, and obtains the task execution sequence adjustment result; The resource ratio analysis submodule analyzes the relationship between the resource consumption of each task and the task execution time based on the task execution order adjustment result, analyzes the resource change trend, and obtains the resource allocation ratio change data; The service task allocation submodule adjusts the execution order of tasks and the resource allocation ratio based on the resource allocation ratio change data, optimizes the task execution process and resource configuration, and obtains an intelligent service task allocation plan.

[0014] On the other hand, a computer intelligent service management method based on network big data is provided, wherein the computer intelligent service management method based on network big data is performed based on the above-mentioned computer intelligent service management system based on network big data, and comprises the following steps: S1: Based on the resource usage and task load data in the network service, extract the task resource consumption ratio and processing time, identify the task completion time, integrate the task completion time and load change trend, and generate resource allocation trend results; S2: Based on the resource allocation trend results, extract task load data and resource utilization, compare task resource consumption and resource availability, analyze consumption and idle ratio, classify load change characteristics, and establish a service operation stage model; S3: Based on the service operation stage model, extract the task load deviation value and resource allocation adjustment parameters, compare the load deviation value with the resource configuration, integrate the adjustment data and the demand parameters, and generate an optimized resource task allocation result; S4: Based on the optimized resource task allocation result, extract the task execution time and resource allocation adjustment data, compare the execution time delay change with the allocation adjustment ratio, correct the allocation process phase data, and obtain the task execution and resource allocation adjustment data; S5: Based on the task execution and resource allocation adjustment data, call the task completion time and resource allocation ratio change data, reorganize the task queue, arrange the execution order, and generate an intelligent service task allocation plan.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By dynamically analyzing the data of resource consumption ratio, task processing time and task completion time, we can accurately identify the changing trends of resource allocation and load, thereby providing more reliable data support for task allocation. Based on the in-depth analysis of the relationship between task load and resource utilization, we can clearly reveal the resource occupation relationship between tasks, ensure the rationality of resource allocation and improve efficiency. On the basis of task priority and resource demand, we can optimize and adjust task allocation deviation and resource allocation, which can not only dynamically balance resource usage, but also significantly improve the execution efficiency and accuracy of services. The analysis of task delay changes and resource utilization changes helps to identify bottlenecks in the task execution process, and by adjusting the execution structure, reduce resource waste and task delays. On the basis of comprehensively considering task completion time and resource allocation ratio, we can dynamically optimize the task execution order, realize intelligent task allocation, improve the overall system operation efficiency and responsiveness, improve resource utilization, optimize task scheduling, and improve the system's response flexibility, ensuring that service quality continues to improve in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a schematic diagram of a computer intelligent service management system based on network big data provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the service status monitoring module in the present invention; Figure 4 A flowchart of the operating state modeling module in the present invention; Figure 5 This is a flow chart of the service optimization module in the present invention; Figure 6 A flowchart of the execution scheme adjustment module in the present invention; Figure 7 A flow chart of a service task allocation module in the present invention; Figure 8 It is a flow chart of a computer intelligent service management method based on network big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The embodiment of the present invention provides a computer intelligent service management system based on network big data, such as Figure 1-2 The schematic diagram of the computer intelligent service management system based on network big data shown in FIG. 1 includes: The service status monitoring module is based on the resource usage and task load data in the network service. By identifying the resource consumption ratio and task processing time, combined with the task completion time, it analyzes the resource allocation and load change trends, and obtains the task resource allocation trend data; The operation status modeling module extracts task load and resource utilization data based on task resource allocation trend data, analyzes the resource occupancy relationship between tasks, identifies the corresponding relationship between tasks and resources based on load change trends, and builds a service operation stage data model; The service optimization module extracts the task load deviation value and resource allocation adjustment data based on the service operation phase data model, compares the task priority and resource demand, and obtains the optimized resource task allocation result through the adjustment of task allocation deviation and resource allocation; The execution plan adjustment module analyzes the impact of task execution adjustment on resource usage based on the optimized resource task allocation results, extracts task delay change and resource utilization change data, modifies the task execution structure, and obtains task execution and resource allocation adjustment data; The service task allocation module is based on task execution and resource allocation adjustment data, adjusts the task execution order according to task priority and resource allocation, extracts task completion time and resource allocation ratio change data, and builds an intelligent service task allocation plan.

[0024] Task resource allocation trend data includes resource consumption ratio, task processing time and task completion time. The service operation phase data model includes task load data, resource utilization data and the correspondence between tasks and resources. The optimized resource task allocation results include task load deviation value, resource allocation adjustment data and the matching relationship between task priority and resource demand. Task execution and resource allocation adjustment data include task delay change data, resource utilization change data and task execution structure adjustment data. The intelligent service task allocation plan includes task completion time, resource allocation ratio change data and task execution sequence.

[0025] Specifically, if Figure 2 , 3 As shown, the service status monitoring module includes: The resource consumption monitoring submodule captures the usage data of CPU, memory, and network bandwidth resources based on the resource usage and task load data in the network service, and performs real-time statistics on resource usage by time period to obtain resource consumption trend data; First, by collecting resource usage logs in the system, record the usage of resources such as CPU, memory, network bandwidth, etc. hour by hour, mark the data with timestamps, organize the data into time series, and classify and summarize the usage of different resource types to identify the consumption trends of various types of resources in different time periods. For example, CPU usage is related to the execution frequency of tasks, and memory usage is related to the size of processed data and task type. Analyze task load data, evaluate the amount of resources consumed by each task, determine the relationship between various resource consumption and tasks, and obtain resource consumption trend data. It can not only present real-time resource usage, but also predict resource demand in the future, thereby providing a basis for subsequent resource scheduling and optimization. The key to the process is to update the load and resource usage trends in real time based on the collected resource data.

[0026] The load analysis submodule is based on resource consumption trend data, through the task processing time and load data, analyzes the load of differentiated tasks within the cycle, extracts load fluctuation data, analyzes the time distribution of task load, and obtains load change trend data; First, the task processing time and load data are annotated and classified according to different task types. For each task type, the time points related to load fluctuations are found through correlation analysis with resource consumption trend data. During the analysis, the focus is on identifying sudden changes in task load during task processing and their correlation with system resource consumption. For example, some tasks will cause a sharp increase in memory usage during execution, resulting in a sharp increase in load. By comparing the task load and processing time, the time distribution of load changes can be identified, especially during high-load periods. Load fluctuation data can be further converted into periodic change trends. The law of task load changes is analyzed through a time series model, and the peak load interval and valley interval of the task are extracted to provide a reference for subsequent task scheduling, resource allocation and load balancing. Through a detailed analysis of periodic load data, the characteristics of load fluctuations are identified and modeled to obtain load change trend data.

[0027] The task completion trend analysis submodule analyzes the relationship between task completion time and task load based on the load change trend data, identifies the changes in resource requirements during task execution in combination with the resource consumption trend, and obtains task resource allocation trend data; First, we need to extract the task completion time and task load data and conduct a correlation analysis. By statistically analyzing the relationship between the task completion time and the load it carries, we can identify the changing pattern of task completion time under different task load levels. For example, when the task load is high, its processing time will increase significantly, especially when the resource consumption is large. The load change trend data is combined with the resource consumption trend data to analyze how the fluctuation of resource consumption during task execution affects the task completion time. The analysis not only helps to understand the changes in resource demand during task execution, but also predicts the amount of resources required for future task execution. It can identify the resource configuration that needs to be adjusted during task execution, ensure that computing resources can be effectively allocated during high-load periods, and avoid the impact of resource bottlenecks on task completion time. Task resource allocation trend data can provide a basis for resource scheduling decisions, especially when tasks are executed concurrently. By reasonably adjusting resource allocation, the overall operating efficiency of the system can be improved. Through the correlation analysis of task load and resource consumption, the resource demand of tasks can be predicted and optimized to ensure that tasks can be successfully completed within the specified time.

[0028] Specifically, if Figure 2 , 4 As shown, the operating status modeling module includes: The resource association analysis submodule extracts the resource occupancy during the execution of each task based on the task resource allocation trend data, monitors the resource demand and usage of each task, and performs time series analysis on resource allocation to obtain a resource occupancy relationship data set; By recording the resource usage of each task during execution, such as CPU, memory, bandwidth, etc., and calculating the difference between the actual resource demand and actual usage of each task, whenever a task starts to execute, the system begins to monitor the resource usage of the task and record the data, including continuous tracking of various resources, by collecting resource change data during task execution, identifying the fluctuation pattern of task demand for resources, arranging the data in chronological order, performing time series analysis, and smoothing the resource usage using the sliding window method. The smoothed data is further used to analyze the relationship between resource usage and task execution efficiency to find resource bottlenecks or over-consumption areas. Through statistical analysis of resource usage data, a resource usage relationship data set for each task execution process is obtained. This data set can reflect the precise matching between tasks and resource usage, and provide necessary information support for subsequent load matching analysis.

[0029] The load matching analysis submodule analyzes the load fluctuation of tasks during execution based on the resource occupancy relationship dataset and the load data of tasks, analyzes the matching degree between task load and resource consumption, identifies the relationship between resource demand and load distribution, and obtains the load and resource matching results; Analyze the relationship between task load and resource usage. By collecting the load fluctuations of each task during execution (such as the task's computing load, memory load, etc.), and combining the data with resource usage data, the system can calculate the matching degree between load and resource consumption. It will sample the task load data in real time and compare it with historical data to reveal the impact of load fluctuations on resource consumption. Load fluctuations are caused by many factors, including the complexity of the task itself, changes in input data, network delays, etc. The matching degree between load and resources will be evaluated based on the correlation between the load change amplitude and resource requirements, and areas with uneven load distribution or insufficient resource utilization during task execution will be identified. By identifying mismatched areas, the load and resource matching results are obtained, so that the resource consumption of each task maintains a high matching degree with the load, thereby improving the overall operation efficiency of the system.

[0030] The data model building submodule analyzes the mutual influence between resources and loads based on the load and resource matching results, combined with the resource allocation and load changes of the tasks, and obtains the data model of the service operation stage; First, we collect and organize historical task execution data, paying special attention to the resource requirements and resource usage of each task under different loads. The data provides the necessary foundation for building the model. By analyzing the relationship between task load and resource usage, we establish a set of quantitative rules to describe the impact of changes in task load on resource usage. For example, if the load of a task increases, we will predict changes in resource demand and adjust the resource allocation strategy based on historical data and resource usage trends. By deeply analyzing the mutual influence between task load and resource allocation, we can obtain a data model for the service operation stage, which can adjust resource allocation in real time during the service operation stage, ensure the stability and efficiency of task execution, reduce resource waste and improve the response speed of computing services.

[0031] Specifically, if Figure 2 , 5 As shown, the service optimization module includes: The task load analysis submodule extracts the task load deviation value based on the service operation phase data model, analyzes the load fluctuation range during the task execution, analyzes the frequency of abnormal load fluctuation, identifies the situation of uneven load distribution, and obtains the task load deviation value; First, the resource consumption data at each moment is obtained from the running status of the task, such as the consumption of CPU, memory, and network bandwidth, and the load deviation value at each moment is calculated. The data helps to identify the load fluctuation range in task execution by comparing the load changes of the task at different stages. For example, if the CPU load fluctuation range of a task is large, the system will mark the existing abnormal load fluctuations according to the amplitude of the load change, and identify potential load imbalance problems in the task by comparing the frequency of load changes. In particular, when the task has frequent and drastic fluctuations in certain stages, it indicates that there is a problem with the resource allocation of the task. Task load analysis can not only detect the fluctuation range, but also evaluate the frequency of abnormal fluctuations, further analyze the reasons behind the fluctuations, and finally help generate task load deviation values ​​for subsequent resource allocation optimization.

[0032] The resource demand matching submodule compares the task priority and corresponding resource consumption based on the task load deviation value, combines the task priority and resource demand data, identifies the imbalance of resource allocation, and obtains the task priority and resource demand data; First, by assigning task priorities and combining them with task load data, we analyze whether the resources actually consumed during task execution match the priority of the task. For example, when the resource demand of a high-priority task obviously exceeds that of a low-priority task, it leads to excessive concentration of resources or insufficient resources. In order to solve the problem, the submodule calculates the resource consumption ratio of each task, compares the difference between actual resource usage and theoretical allocation, and identifies which tasks have deviations in resource allocation. If the priority of a task is too low and the resource consumption is too high, the system will issue a warning and re-evaluate whether the priority adjustment of the task needs to be carried out simultaneously to ensure a reasonable match between the priority of the task and the resource demand, and to ensure that the overall resource allocation of the system is more balanced and effective.

[0033] The task resource optimization submodule adjusts the task resource allocation based on the task priority and resource demand data, compares the matching degree between tasks and resources, calculates the difference value of task resource allocation, optimizes resource configuration, and obtains the optimized resource task allocation result; Calculate the difference in task resource allocation according to the formula: ; Where D represents the difference value of task resource allocation, represents the weight coefficient of the i-th task, represents the priority of the i-th task, represents the demand value of the i-th resource, and n represents the total number of tasks; Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the difference between the matching degree of tasks and resources. The result is used to optimize the allocation of tasks and resources to improve resource utilization efficiency and task execution effect. This formula calculates the difference between task priority and resource demand by weight, and comprehensively considers the priority of each task and the demand of each resource. For the The weight coefficient of each task reflects the priority of the task. This value is determined by the importance of the task and is set by monitoring the urgency of the task execution. Assume The value of is 0.8, which means that the task has a higher urgency in the priority ranking and needs to be allocated resources first; For the The priority of a task reflects the urgency of task processing. This value is based on the priority data monitored in the scheduling system of the actual task. The value of is 5, which means that the task has a higher priority in resource allocation. The value is set by the priority setting in the task allocation system; For the The resource requirement value indicates the necessity of the resource for task completion. This value is determined by the type and quantity of resources required for the task and is calculated using the resource requirement table. Assume The value of 3 indicates that the resource demand for tasks is strong, and the resource demand comes from specific task scheduling and historical data analysis; n is the total number of tasks, indicating the number of tasks that need to allocate resources. Assuming n=4, it means there are 4 tasks that need to be allocated resources; Substitute the parameters into the formula for calculation: ; Assume that the task parameters are as follows: Calculation process: ; ; The result 1.45 indicates the degree of difference in the matching between tasks and resources. The larger the value, the greater the difference in the matching between tasks and resources, and the greater the potential for optimization. This result can be used to further adjust resource allocation to ensure a better match between tasks and resources.

[0034] Specifically, if Figure 2 , 6 As shown, the execution scheme adjustment module includes: The task delay analysis submodule extracts the task delay data based on the optimized resource task allocation results, analyzes the delay fluctuation range of each task, monitors the delay changes during task execution, identifies the deviation of the delay time, and obtains the task delay change results; First, extract the delay data of each task from the resource task allocation results. The delay data includes the difference between the actual execution time of the task and the expected execution time. The timestamp can be used to record the start and end time of the task, and the delay is obtained by calculating the difference between the two. The delay fluctuation range of all tasks is analyzed. The delay fluctuation of each task is obtained by calculating the standard deviation and the average value of the delay data of each task. The deviation of the task delay is the difference between the actual delay value and the average delay value. When the fluctuation of the task delay exceeds a certain threshold, the delay deviation of the task will be identified. The delay changes during the execution of the task can be monitored. The real-time data acquisition system can be used for dynamic tracking and periodic update of the delay value to timely discover and respond to any abnormal changes in the task execution, ensure that the task is executed as expected, obtain the task delay change results, list the delay change trends of each task, abnormal tasks and their causes, and provide a basis for subsequent adjustments.

[0035] The resource utilization change submodule collects resource utilization data during task execution based on the task delay change results, analyzes resource utilization changes before and after task execution, identifies the change trend of resource consumption, and obtains a resource utilization change data set; First, all data related to resource utilization during task execution are collected, including CPU usage, memory occupancy, storage I / O, network bandwidth usage, etc. By comparing the resource utilization before and after task execution, the resource consumption of each task during execution is analyzed. The specific calculation method is to compare the resource consumption data before and during task execution to identify the difference in resource usage of each task before and after execution. The difference in resource usage before and after task execution is the changing trend of resource consumption. According to the changing trend, it is identified which resource consumption has changed significantly during task execution. In order to better describe the changes in resource consumption, a change curve chart can be drawn to show the fluctuations in resource consumption of each task. This data set will cover the resource consumption of each task at different time points, further analyze the changing trend of resources, identify potential resource waste or bottlenecks, and obtain a resource utilization change data set, which provides an important basis for the subsequent execution structure correction.

[0036] The execution structure correction submodule adjusts the task execution structure based on the resource utilization change data set, corrects the resource allocation and execution order between tasks, calculates the optimized task execution structure adjustment value, and obtains the task execution and resource allocation adjustment data; Calculate the optimized task execution structure adjustment value according to the formula: ; Among them, Q represents the optimized task execution structure adjustment value, represents the task weight coefficient, Represents resource availability, Represents resource utilization efficiency, Represents the task execution time, represents the order weight between tasks, Represents the scheduled task execution time, is the adjustment factor, The flexibility factor for adjusting the order of task execution; Detailed explanation of the formula and the process of formula calculation and derivation: This formula is used to calculate the optimal value (Q) of resource allocation and execution order of the task execution structure. By weighted adjustment of different parameters, the resource utilization efficiency and task execution order in the task execution process are optimized. The result will be used as the adjusted task execution and resource allocation data value for optimization decision-making. Represents the task weight coefficient, which measures the relative importance of each task in the overall execution structure. This value reflects the degree of influence of the task on the system optimization goal. The task weight coefficient is determined by analyzing the task's resource requirements, execution time, and the system's setting of task priority. The task weight value is set according to the task priority. For example, the weight of a high-priority task is larger, while the weight of a low-priority task is smaller. Set the weight of task 1 Set to 0.8, the weight of task 2 is 0.2; Represents resource availability, which indicates the proportion of available resources when executing a task. Resource availability is obtained by dividing the available quantity or total quantity of each resource monitored in real time by the resource management system by the demand of the current task. Resource availability is quantified by dynamically evaluating the task resource demand and comparing it with the current available resources. The resource availability of task 1 is set to 0.9, and the resource availability of task 2 is set to 0.5. Represents resource utilization efficiency, indicating the degree to which resources are effectively utilized during task execution. This value is calculated based on historical task data, that is, the ratio of the actual amount of resources used by the task to the resource allocation. Set the resource utilization efficiency of task 1 is 0.85, and task 2 is 0.75; Represents the task execution time, which indicates the total time from the start to the completion of the task. The task execution time can be obtained through the system scheduler or historical data. It is obtained by the time difference between the start and end of the task. Set the execution time of Task 1 is 12 hours, and Task 2 is 8 hours; Represents the order weight between tasks, quantifying the importance of priority and execution order between tasks. This value can be set by the priority set by the system or based on the output results of the resource optimization algorithm. For example, task 1 has a higher priority, so is 1, the priority of task 2 is lower, set is 0.5; Represents the scheduled task execution time, indicating the target execution time of the task. The scheduled execution time is set based on the historical data of the task, system requirements, or the complexity of the task. Set the scheduled execution time of Task 1 is 10 hours, and Task 2 is 7 hours; The coefficient is used to adjust the flexibility of task resource allocation and execution order optimization. The coefficient is determined through experiments or simulations to ensure the balance of the optimization process. is set to 1.2 to adapt to the complexity of task resources and execution order; To adjust the flexibility coefficient of task execution order adjustment, this coefficient is used to fine-tune the execution order of tasks. It is set according to the order priority of tasks and their impact on the optimization effect. is 0.3, which means that the flexibility of task order adjustment is low; According to the above set values, substitute into the formula for calculation: ; ; ; The result shows that the optimization value of resource allocation and execution order of task execution structure is 1.178, which reflects the optimized task execution structure, including the comprehensive influence of task resource utilization efficiency, task execution time and order adjustment.

[0037] Specifically, if Figure 2 , 7 As shown, the service task allocation module includes: The task order adjustment submodule adjusts the execution order of tasks based on the task execution and resource allocation adjustment data, evaluates the resource requirements and available resources of tasks, analyzes the impact of resource allocation during task execution, optimizes the execution order of tasks, and obtains the task execution order adjustment results; First, obtain the resource information required for each task during execution, and conduct comparative analysis based on the actual available resources. By evaluating the matching of resource requirements and resource availability, identify tasks with excessive or insufficient resources, obtain the execution status of tasks in real time, consider the urgency and dependencies of task execution, and dynamically adjust the priority of tasks based on factors to avoid waste of resources and delays. The order of task execution will be affected by resource constraints, and its start and end time needs to be adjusted, or a more efficient resource scheduling strategy needs to be introduced. By continuously evaluating resource changes, the order of task execution can be optimized to ensure that resources are used to the greatest extent. For example, when resources are tight, according to the importance of tasks, the execution of some non-critical tasks can be selectively postponed, and the execution of core tasks can be prioritized to improve overall work efficiency, and the results of task execution order adjustment are finally output.

[0038] The resource ratio analysis submodule analyzes the relationship between the resource consumption of each task and the task execution time based on the task execution order adjustment results, analyzes the resource change trend, and obtains the resource allocation ratio change data; According to the adjusted task execution order, the resource consumption and time spent on each task during execution will be calculated respectively. According to the real-time data and the historical execution data, the resource consumption of each task will be accurately estimated, and the efficiency and time cost of resource use in the execution of each task will be analyzed. By comparing the resource demand and consumption between tasks, we can draw optimization suggestions for resource allocation and analyze the changing trend of resource consumption. At each stage of task execution, the efficiency of resource utilization changes dynamically and is affected by external factors such as network conditions and hardware performance. During the task execution process, the changes in resources will be continuously monitored, and the resource allocation ratio will be adjusted in time to ensure the optimal execution of tasks under different resource conditions. By analyzing the changes in resource ratios, the resource allocation ratio change data can be obtained, which can provide more accurate data support for subsequent task allocation and provide a decision-making basis for intelligent service management.

[0039] The service task allocation submodule adjusts the execution order of tasks and resource allocation ratio based on the resource allocation ratio change data, optimizes the task execution process and resource allocation, and obtains an intelligent service task allocation plan; It will dynamically adjust the execution order of tasks based on the resource ratio change data, and make the best configuration based on the task's resource requirements and available resources. It will analyze the different resource types required for the execution of each task, consider the resource demand of each task, and appropriately optimize the task order. It will also continuously analyze the actual execution of each task, adjust the priority of tasks based on real-time feedback, and ensure that each task can be executed under the most appropriate resource configuration. Through intelligent scheduling algorithms, it can analyze the bottlenecks in the task execution process based on historical data, and adjust the resource allocation ratio in real time to ensure the optimal use of resources. The system outputs an optimized intelligent service task allocation plan, which can complete task scheduling and resource allocation more efficiently, thereby improving the efficiency and quality of overall service management.

[0040] See also Figure 8 The computer intelligent service management method based on network big data is executed based on the above-mentioned computer intelligent service management system based on network big data, and includes the following steps: S1: Based on the resource usage and task load data in the network service, extract the task resource consumption ratio and processing time, identify the task completion time, integrate the task completion time and load change trend, and generate resource allocation trend results; S2: Based on the resource allocation trend results, extract task load data and resource utilization, compare task resource consumption and resource availability, analyze the consumption and idle ratio, classify load change characteristics, and establish a service operation stage model; S3: Based on the service operation stage model, extract the task load deviation value and resource allocation adjustment parameters, compare the load deviation value with the resource configuration, integrate the adjustment data and demand parameters, and generate the optimized resource task allocation result; S4: Based on the optimized resource task allocation results, extract the task execution time and resource allocation adjustment data, compare the execution time delay change with the allocation adjustment ratio, correct the allocation process phase data, and obtain the task execution and resource allocation adjustment data; S5: Based on the task execution and resource allocation adjustment data, call the task completion time and resource allocation ratio change data, reorganize the task queue, arrange the execution order, and generate an intelligent service task allocation plan.

[0041] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A computer intelligent service management system based on network big data, characterized in that: The system comprises: The service status monitoring module is based on the resource usage and task load data in the network service. By identifying the resource consumption ratio and task processing time, combined with the task completion time, it analyzes the resource allocation and load change trends, and obtains the task resource allocation trend data; The operation status modeling module extracts the task load and resource utilization data based on the task resource allocation trend data, analyzes the resource occupation relationship between tasks, identifies the corresponding association between tasks and resources in combination with the load change trend, and builds a service operation stage data model; The service optimization module extracts the task load deviation value and resource allocation adjustment data based on the service operation phase data model, compares the task priority and resource demand, and obtains the optimized resource task allocation result through the adjustment of task allocation deviation and resource allocation; The execution scheme adjustment module analyzes the impact of task execution adjustment on resource usage based on the optimized resource task allocation result, extracts task delay change and resource utilization change data, corrects the task execution structure, and obtains task execution and resource allocation adjustment data; The service task allocation module adjusts the task execution order according to the task priority and resource allocation based on the task execution and resource allocation adjustment data, extracts the task completion time and resource allocation ratio change data, and constructs an intelligent service task allocation plan.

2. According to claim 1, the computer intelligent service management system based on network big data is characterized in that: The task resource allocation trend data includes resource consumption ratio, task processing time and task completion time; the service operation phase data model includes task load data, resource utilization data and the correspondence between tasks and resources; the optimized resource task allocation result includes task load deviation value, resource allocation adjustment data and task priority and resource demand matching relationship; the task execution and resource allocation adjustment data includes task delay change data, resource utilization change data and task execution structure adjustment data; the intelligent service task allocation plan includes task completion time, resource allocation ratio change data and task execution order.

3. The computer intelligent service management system based on network big data according to claim 1 is characterized in that: The service status monitoring module includes: The resource consumption monitoring submodule captures the usage data of CPU, memory, and network bandwidth resources based on the resource usage and task load data in the network service, and performs real-time statistics on resource usage by time period to obtain resource consumption trend data; The load analysis submodule analyzes the load conditions of differentiated tasks within a cycle based on the resource consumption trend data, through the processing time and load data of the tasks, extracts the load fluctuation data, analyzes the time distribution of the task load, and obtains the load change trend data; The task completion trend analysis submodule analyzes the relationship between task completion time and task load based on the load change trend data, identifies the changes in resource requirements during task execution in combination with the resource consumption trend, and obtains task resource allocation trend data.

4. The computer intelligent service management system based on network big data according to claim 1 is characterized in that: The operating status modeling module includes: The resource association analysis submodule extracts the resource occupancy during the execution of each task based on the task resource allocation trend data, monitors the resource demand and usage of each task, and performs time series analysis on resource allocation to obtain a resource occupancy relationship data set; The load matching analysis submodule analyzes the load fluctuation of the task during execution based on the resource occupancy relationship data set and the load data of the task, analyzes the matching degree between the task load and resource consumption, identifies the relationship between resource demand and load distribution, and obtains the load and resource matching result; The data model building submodule analyzes the mutual influence between resources and load based on the load and resource matching results, combined with the resource allocation and load changes of the task, and obtains the service operation stage data model.

5. The computer intelligent service management system based on network big data according to claim 1 is characterized by: The service optimization module includes: The task load analysis submodule extracts the load deviation value of the task based on the service operation phase data model, analyzes the fluctuation range of the load during the execution of the task, analyzes the frequency of abnormal load fluctuation, identifies the situation of uneven load distribution, and obtains the task load deviation value; The resource demand matching submodule compares the task priority and the corresponding resource consumption based on the task load deviation value, combines the task priority and the resource demand data, identifies the imbalance of resource allocation, and obtains the task priority and resource demand data; The task resource optimization submodule adjusts the task resource allocation based on the task priority and resource demand data, compares the matching degree between tasks and resources, calculates the difference value of task resource allocation, optimizes resource configuration, and obtains the optimized resource task allocation result.

6. The computer intelligent service management system based on network big data according to claim 5 is characterized in that: Calculate the difference value of the task resource allocation according to the formula: ； Where D represents the difference value of task resource allocation, represents the weight coefficient of the i-th task, represents the priority of the i-th task, represents the demand value of the i-th resource, and n represents the total number of tasks.

7. The computer intelligent service management system based on network big data according to claim 1 is characterized in that: The execution scheme adjustment module includes: The task delay analysis submodule extracts the task delay data based on the optimized resource task allocation result, analyzes the delay fluctuation range of each task, monitors the delay change during task execution, identifies the deviation of the delay time, and obtains the task delay change result; The resource utilization change submodule collects resource utilization data during task execution based on the task delay change result, analyzes resource utilization changes before and after task execution, identifies the change trend of resource consumption, and obtains a resource utilization change data set; The execution structure correction submodule adjusts the task execution structure based on the resource utilization change data set, corrects the resource allocation and execution order between tasks, calculates the optimized task execution structure adjustment value, and obtains task execution and resource allocation adjustment data.

8. The computer intelligent service management system based on network big data according to claim 7 is characterized in that: Calculate the optimized task execution structure adjustment value according to the formula: ； Among them, Q represents the optimized task execution structure adjustment value, represents the task weight coefficient, Represents resource availability, Represents resource utilization efficiency, Represents the task execution time, represents the order weight between tasks, Represents the scheduled task execution time, is the adjustment factor, The flexibility factor is used to adjust the order of task execution.

9. The computer intelligent service management system based on network big data according to claim 1 is characterized in that: The service task allocation module includes: The task sequence adjustment submodule adjusts the execution sequence of tasks based on the task execution and resource allocation adjustment data, evaluates the resource requirements and available resources of the tasks, analyzes the impact of resource allocation during task execution, optimizes the execution sequence of tasks, and obtains the task execution sequence adjustment result; The resource ratio analysis submodule analyzes the relationship between the resource consumption of each task and the task execution time based on the task execution order adjustment result, analyzes the resource change trend, and obtains the resource allocation ratio change data; The service task allocation submodule adjusts the execution order of tasks and the resource allocation ratio based on the resource allocation ratio change data, optimizes the task execution process and resource configuration, and obtains an intelligent service task allocation plan.

10. A computer intelligent service management method based on network big data, characterized in that: According to any one of claims 1 to 9, the computer intelligent service management system based on network big data is implemented. The following steps are involved: S1: Based on the resource usage and task load data in the network service, extract the task resource consumption ratio and processing time, identify the task completion time, integrate the task completion time and load change trend, and generate resource allocation trend results; S2: Based on the resource allocation trend results, extract task load data and resource utilization, compare task resource consumption and resource availability, analyze consumption and idle ratio, classify load change characteristics, and establish a service operation stage model; S3: Based on the service operation stage model, extract the task load deviation value and resource allocation adjustment parameters, compare the load deviation value with the resource configuration, integrate the adjustment data and the demand parameters, and generate an optimized resource task allocation result; S4: Based on the optimized resource task allocation result, extract the task execution time and resource allocation adjustment data, compare the execution time delay change with the allocation adjustment ratio, correct the allocation process phase data, and obtain the task execution and resource allocation adjustment data; S5: Based on the task execution and resource allocation adjustment data, call the task completion time and resource allocation ratio change data, reorganize the task queue, arrange the execution order, and generate an intelligent service task allocation plan.

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