A simulation resource scheduling method based on knowledge graph

Abstract

The present invention relates to the technical field of simulation resource data processing, and in particular to a simulation resource scheduling method based on knowledge graph. The method comprises the following steps: obtaining simulation system task resource information data and performing resource characteristic connection prediction analysis and knowledge graph connection construction to obtain a simulation system task resource knowledge graph; performing task resource demand analysis and resource status monitoring processing according to the simulation system task resource knowledge graph to obtain simulation system task resource status information data; performing task priority statistical analysis and resource dynamic matching scheduling according to the simulation system task resource status information data to obtain a simulation system task resource dynamic scheduling result; performing simulation task and resource dynamic change monitoring and real-time scheduling optimization on the simulation system task resource information data to obtain a simulation system task resource real-time scheduling optimization result. The present invention can realize intelligent scheduling and optimal allocation of simulation resources.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation resource data processing, and in particular to a simulation resource scheduling method based on knowledge graph. Background Art

[0002] The simulation resource scheduling method based on knowledge graph is a method that combines knowledge graph technology with corresponding computer processing technology, aiming to solve the problem of resource scheduling in simulation systems. In modern society, various systems such as traffic simulation systems, computer simulation systems, 3D simulation systems, etc. involve the reasonable scheduling and utilization of simulation resources. This method uses knowledge graph technology to model and represent various resources, tasks, constraints and other knowledge in the form of graphs, thereby forming a knowledge graph containing rich domain knowledge, and through the resource scheduling analysis based on this knowledge graph, it simulates the scheduling process of resources in the real simulation system, and through simulating different resource allocation schemes and strategies, evaluates their impact on system performance, and thus finds the optimal resource scheduling scheme. However, traditional simulation resource scheduling methods are usually based on static rules or simple scheduling optimization algorithms, and have the problems of low resource utilization efficiency and difficulty in meeting complex and changing task requirements. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a simulation resource scheduling method based on knowledge graph to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a simulation resource scheduling method based on knowledge graph includes the following steps:

[0005] Step S1: Acquire simulation system task resource information data, and perform resource characteristic connection prediction analysis on the simulation system task resource information data to obtain simulation system task resource characteristic connection relationship data; perform knowledge graph connection construction on the simulation system task resource information data based on the simulation system task resource characteristic connection relationship data to obtain a simulation system task resource knowledge graph;

[0006] Step S2: performing task resource demand analysis on the simulation system task resource information data according to the simulation system task resource knowledge graph to obtain the simulation system task resource demand information data; performing resource status monitoring processing on the simulation system task resource demand information data to obtain the simulation system task resource status information data;

[0007] Step S3: performing task priority statistics analysis according to the simulation system task resource status information data to generate a simulation system task resource requirement priority sequence; performing resource dynamic matching scheduling on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task resource dynamic scheduling result;

[0008] Step S4: Monitor the dynamic changes of simulation tasks and resources on the simulation system task resource information data to obtain the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources; based on the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources, perform real-time scheduling optimization on the dynamic scheduling results of the simulation system task resources to obtain the real-time scheduling optimization results of the simulation system task resources.

[0009] The present invention first obtains necessary simulation task resource information from the simulation system, which can provide basic data for subsequent analysis and optimization, including collecting configuration, status, usage and other information of various resources from the simulation system, and providing data support for monitoring and management of the overall operation of the simulation system. By accurately obtaining task resource information data, it can provide necessary basis for subsequent knowledge graph construction, resource scheduling and optimization, thereby improving the resource scheduling efficiency and reliability of the simulation system. By performing resource characteristic connection prediction analysis on the simulation system task resource information data, the analysis and prediction of the association relationship between the resources required for the simulation system tasks can be realized, and potential resource connection patterns and laws can be discovered to provide guidance for subsequent resource optimization and configuration. This step involves mining and modeling the relationship between the resources required for the simulation system tasks, as well as the prediction and analysis of resource connection characteristics, thereby providing a basis for the reasonable allocation and optimization of system resources. By deeply understanding the connection relationship between resources, resources can be better utilized and the overall efficiency and performance of the system can be improved. At the same time, by connecting the relationship data of the simulation system task resource characteristics to the simulation system task resource information data, a knowledge graph is constructed to present the relationship between resources in the form of a graph, thereby providing intuitive visualization support for subsequent resource management and optimization. The construction process of the knowledge graph connection involves the extraction, modeling and visualization of the relationship between resources, making the relationship between system resources clear at a glance. Through the construction of the knowledge graph, users can better understand the connection relationship between simulation task resources and discover potential optimization space, thereby providing data support for the subsequent resource scheduling process. Secondly, by analyzing the task resource demand of the simulation system task resource information data according to the simulation system task resource knowledge graph, in this step, it is possible to predict and analyze future resource demands based on the existing simulation system task resource demand pattern data and use predictive analysis technology to form a comprehensive simulation system task resource demand information data set. This data set is a predictive analysis based on historical data and demand pattern recognition results, which provides an important reference for the system's resource planning and scheduling, enabling the system to respond to changes and fluctuations in resource demand in advance, thereby improving the system's flexibility and responsiveness. The key to this step is to provide the simulation system with the ability to predict and analyze resource demands, enabling the system to manage and schedule resources more intelligently, thereby providing basic data support for subsequent processing processes.By performing resource status monitoring on the task resource demand information data of the simulation system, the demand status data related to computing resources and storage resources can be extracted from it. This data set integrates the demand status information of computing resources and storage resources, thereby providing comprehensive data support for subsequent resource management and scheduling. The key to this step is that it can provide a comprehensive understanding of the resource status of the simulation system, so that the simulation system can perform resource scheduling and management more intelligently, thereby improving the efficiency and performance of the subsequent scheduling process. Then, by performing task priority statistical analysis based on the task resource status information data of the simulation system, it means that the simulation system can sort and adjust the priority of its simulation task resource demand according to the completion status and waiting time of the task. Through this analysis, resources can be allocated more effectively, task execution efficiency can be improved, task delay can be reduced, and system requirements can be met to the greatest extent. By performing resource dynamic matching scheduling on the priority sequence of the task resource demand of the simulation system based on the task resource demand information data of the simulation system, this processing process is to dynamically adjust resource allocation according to the current resource status of the simulation system and the priority of the task to meet the requirements of the system tasks to the greatest extent. Through dynamic resource matching and scheduling, we can more flexibly respond to complex and changing task requirements, thereby improving resource utilization, optimizing system performance, and ensuring the smooth execution of tasks. Finally, by monitoring the dynamic changes of simulation tasks on the task resource information data of the simulation system, we can capture the status changes of tasks in real time, including the addition, deletion, and modification of tasks. This monitoring process can help system managers to timely understand the real-time changes of simulation system tasks so as to make timely responses and adjustments to ensure that the simulation system can run smoothly, thereby providing important data support for the subsequent resource scheduling and optimization of the system. In addition, by monitoring the dynamic changes of simulation system task resource information data, we can monitor the dynamic changes of task resources in real time, including the increase, reduction, and reallocation of resources. This monitoring process can help to timely adjust the resource allocation strategy to adapt to the changing needs of resources when the simulation system tasks are running, thereby ensuring that the simulation system tasks can run continuously and stably. In addition, by optimizing the dynamic scheduling results of the simulation system task resources based on the dynamic change data of the real-time status of the simulation system tasks and the dynamic change data of the simulation system task resources, the allocation and scheduling of task resources can be adjusted in time to adapt to the changing needs during system operation. This real-time scheduling optimization can help to respond more flexibly to changes in the simulation system operation, ensuring that the simulation system can continue to run efficiently, thereby improving the stability and reliability of the simulation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 It is a schematic diagram of the steps of the simulation resource scheduling method based on the knowledge graph of the present invention;

[0012] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0013] Figure 3 for Figure 2 Detailed step flow chart of step S14 in FIG. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0016] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0017] To achieve this, please refer to Figures 1 to 3 The present invention provides a simulation resource scheduling method based on knowledge graph, the method comprising the following steps:

[0018] Step S1: Acquire simulation system task resource information data, and perform resource characteristic connection prediction analysis on the simulation system task resource information data to obtain simulation system task resource characteristic connection relationship data; perform knowledge graph connection construction on the simulation system task resource information data based on the simulation system task resource characteristic connection relationship data to obtain a simulation system task resource knowledge graph;

[0019] Step S2: performing task resource demand analysis on the simulation system task resource information data according to the simulation system task resource knowledge graph to obtain the simulation system task resource demand information data; performing resource status monitoring processing on the simulation system task resource demand information data to obtain the simulation system task resource status information data;

[0020] Step S3: performing task priority statistics analysis according to the simulation system task resource status information data to generate a simulation system task resource requirement priority sequence; performing resource dynamic matching scheduling on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task resource dynamic scheduling result;

[0021] Step S4: Monitor the dynamic changes of simulation tasks and resources on the simulation system task resource information data to obtain the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources; based on the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources, perform real-time scheduling optimization on the dynamic scheduling results of the simulation system task resources to obtain the real-time scheduling optimization results of the simulation system task resources.

[0022] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of the simulation resource scheduling method based on the knowledge graph of the present invention. In this example, the simulation resource scheduling method based on the knowledge graph includes the following steps:

[0023] Step S1: Acquire simulation system task resource information data, and perform resource characteristic connection prediction analysis on the simulation system task resource information data to obtain simulation system task resource characteristic connection relationship data; perform knowledge graph connection construction on the simulation system task resource information data based on the simulation system task resource characteristic connection relationship data to obtain a simulation system task resource knowledge graph;

[0024] The embodiment of the present invention obtains necessary simulation task resource information from the simulation system by using system logs, monitoring tools or database queries, including configuration, status, usage and other information of various task resources, so as to ensure the integrity and accuracy of resource information data, thereby obtaining simulation system task resource information data. Secondly, by analyzing and identifying the configuration and usage types of various resources in the simulation system task, at the same time, by combining the configuration and usage types obtained by the analysis, the corresponding simulation system task resources in the simulation system task resource information data are subjected to characteristic connection prediction analysis, so as to predict the connection relationship between different simulation system task resources, and realize the analysis and prediction of the association relationship between resources required for the simulation system task, including the dependency relationship between different computing resources or the interconnection relationship between storage resources and computing resources, etc. Then, by combining the predicted characteristic connection relationship of the simulation system task resources, the knowledge relationship of the corresponding simulation system task resources in the simulation system task resource information data is connected and constructed as a knowledge graph, so that the connection relationship between the simulation system task resources is represented in the form of a graph, and the relevance and dependency relationship between resources in the simulation system task are more intuitively displayed, and finally the simulation system task resource knowledge graph is obtained.

[0025] Step S2: performing task resource demand analysis on the simulation system task resource information data according to the simulation system task resource knowledge graph to obtain the simulation system task resource demand information data; performing resource status monitoring processing on the simulation system task resource demand information data to obtain the simulation system task resource status information data;

[0026] The embodiment of the present invention uses a pattern recognition analysis method in combination with the constructed simulation system task resource knowledge graph to analyze and identify the corresponding simulation system task resources in the simulation system task resource information data, so as to identify and analyze the patterns and rules between the task resources, including the mutual dependence and demand patterns between the resources, and performs a predictive analysis of the resource demand of the corresponding simulation system task resources in the simulation system task resource information data by combining the task resource demand pattern obtained by the analysis, so as to predict the future resource demand of the corresponding simulation system task, including computing resources and storage resources, and enable it to respond to changes and fluctuations in resource demand in advance, while monitoring and integrating the demand status information of different types of resources, and more comprehensively understanding the usage and demand trends of the simulation system task resources, so as to finally obtain the simulation system task resource status information data.

[0027] Step S3: performing task priority statistics analysis according to the simulation system task resource status information data to generate a simulation system task resource requirement priority sequence; performing resource dynamic matching scheduling on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task resource dynamic scheduling result;

[0028] The embodiment of the present invention uses a corresponding prediction analysis algorithm to analyze the simulation system task resource status information data obtained by analysis, so as to predict and analyze factors such as the type of task in the simulation system, resource requirements, and the current load condition of the system, and predicts the completion status of the corresponding simulation system task in combination with the relevant factors obtained by analysis. At the same time, the completion time and delay time of each simulation system task are obtained by using a time statistical method, and the completion time and delay time of each simulation system task are calculated and accumulated by using a difference calculation method to statistically obtain the waiting time for the completion of the corresponding simulation system task, and the waiting time obtained by statistics is calculated by arithmetic average. Secondly, the priority statistical analysis of the simulation system task resource status information data is performed by combining the task completion status and the average waiting time for task completion obtained by analysis, so as to comprehensively consider the urgency of the simulation system task. and the degree of influence on the simulation system, determine the priority ranking of resources in each simulation system task, and then, perform resource matching analysis on the corresponding simulation system tasks in the priority ranking sequence by combining the resource demand status corresponding to the corresponding simulation system tasks in the simulation system task resource demand information data obtained by analysis, so as to fully consider the importance, urgency and resource demand of the simulation system tasks, and effectively allocate system task resources, and use intelligent scheduling algorithms and technologies according to the resource matching results obtained after matching to design the optimal resource scheduling strategy for different simulation system task priorities and resource requirements, and perform real-time resource scheduling processing on the corresponding simulation system tasks in the priority ranking sequence according to the designed resource intelligent scheduling strategy, so as to dynamically adjust the resource allocation of the simulation system tasks, and meet the real-time requirements of the simulation system task operation, and finally obtain the dynamic scheduling result of the simulation system task resources.

[0029] Step S4: Monitor the dynamic changes of simulation tasks and resources on the simulation system task resource information data to obtain the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources; based on the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources, perform real-time scheduling optimization on the dynamic scheduling results of the simulation system task resources to obtain the real-time scheduling optimization results of the simulation system task resources.

[0030] The embodiment of the present invention monitors the task resource information data of the simulation system by using the task monitoring method, so as to capture the dynamic changes of the simulation system tasks in real time according to the change information of its resources, including monitoring the addition, deletion, modification, etc. of the simulation tasks, so as to timely understand the real-time state dynamic changes of the simulation system tasks, thereby obtaining the real-time state dynamic change data of the simulation system tasks. At the same time, the task resource information data of the simulation system is monitored by using the resource change monitoring method, so as to monitor the dynamic changes of the task resources in real time, including the increase, reduction, and reallocation of resources, etc., so as to adapt to the change requirements of the resources when the simulation system tasks are running, thereby obtaining the dynamic change data of the task resources of the simulation system. Secondly, by combining the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources obtained by analysis, a corresponding resource scheduling optimization strategy is generated to dynamically adjust the allocation and scheduling of the corresponding simulation system task resources according to the real-time task status and dynamic changes of resources. Then, by combining the optimized resource scheduling optimization strategy, the dynamic scheduling results of the simulation system task resources of the corresponding simulation task are re-scheduled and optimized in real time. The task resources are optimized in real time according to the resource scheduling optimization strategy. The allocation and scheduling of task resources are adjusted in time to adapt to the changing needs during the operation of the simulation system task, and finally the real-time scheduling optimization results of the simulation system task resources are obtained.

[0031] The present invention first obtains necessary simulation task resource information from the simulation system, which can provide basic data for subsequent analysis and optimization, including collecting configuration, status, usage and other information of various resources from the simulation system, and providing data support for monitoring and management of the overall operation of the simulation system. By accurately obtaining task resource information data, it can provide necessary basis for subsequent knowledge graph construction, resource scheduling and optimization, thereby improving the resource scheduling efficiency and reliability of the simulation system. By performing resource characteristic connection prediction analysis on the simulation system task resource information data, the analysis and prediction of the association relationship between the resources required for the simulation system tasks can be realized, and potential resource connection patterns and laws can be discovered to provide guidance for subsequent resource optimization and configuration. This step involves mining and modeling the relationship between the resources required for the simulation system tasks, as well as the prediction and analysis of resource connection characteristics, thereby providing a basis for the reasonable allocation and optimization of system resources. By deeply understanding the connection relationship between resources, resources can be better utilized and the overall efficiency and performance of the system can be improved. At the same time, by connecting the relationship data of the simulation system task resource characteristics to the simulation system task resource information data, a knowledge graph is constructed to present the relationship between resources in the form of a graph, thereby providing intuitive visualization support for subsequent resource management and optimization. The construction process of the knowledge graph connection involves the extraction, modeling and visualization of the relationship between resources, making the relationship between system resources clear at a glance. Through the construction of the knowledge graph, users can better understand the connection relationship between simulation task resources and discover potential optimization space, thereby providing data support for the subsequent resource scheduling process. Secondly, by analyzing the task resource demand of the simulation system task resource information data according to the simulation system task resource knowledge graph, in this step, it is possible to predict and analyze future resource demands based on the existing simulation system task resource demand pattern data and use predictive analysis technology to form a comprehensive simulation system task resource demand information data set. This data set is a predictive analysis based on historical data and demand pattern recognition results, which provides an important reference for the system's resource planning and scheduling, enabling the system to respond to changes and fluctuations in resource demand in advance, thereby improving the system's flexibility and responsiveness. The key to this step is to provide the simulation system with the ability to predict and analyze resource demands, enabling the system to manage and schedule resources more intelligently, thereby providing basic data support for subsequent processing processes.By performing resource status monitoring on the task resource demand information data of the simulation system, the demand status data related to computing resources and storage resources can be extracted from it. This data set integrates the demand status information of computing resources and storage resources, thereby providing comprehensive data support for subsequent resource management and scheduling. The key to this step is that it can provide a comprehensive understanding of the resource status of the simulation system, so that the simulation system can perform resource scheduling and management more intelligently, thereby improving the efficiency and performance of the subsequent scheduling process. Then, by performing task priority statistical analysis based on the task resource status information data of the simulation system, it means that the simulation system can sort and adjust the priority of its simulation task resource demand according to the completion status and waiting time of the task. Through this analysis, resources can be allocated more effectively, task execution efficiency can be improved, task delay can be reduced, and system requirements can be met to the greatest extent. By performing resource dynamic matching scheduling on the priority sequence of the task resource demand of the simulation system based on the task resource demand information data of the simulation system, this processing process is to dynamically adjust resource allocation according to the current resource status of the simulation system and the priority of the task to meet the requirements of the system tasks to the greatest extent. Through dynamic resource matching and scheduling, we can more flexibly respond to complex and changing task requirements, thereby improving resource utilization, optimizing system performance, and ensuring the smooth execution of tasks. Finally, by monitoring the dynamic changes of simulation tasks on the task resource information data of the simulation system, we can capture the status changes of tasks in real time, including the addition, deletion, and modification of tasks. This monitoring process can help system managers to timely understand the real-time changes of simulation system tasks so as to make timely responses and adjustments to ensure that the simulation system can run smoothly, thereby providing important data support for the subsequent resource scheduling and optimization of the system. In addition, by monitoring the dynamic changes of simulation system task resource information data, we can monitor the dynamic changes of task resources in real time, including the increase, reduction, and reallocation of resources. This monitoring process can help to timely adjust the resource allocation strategy to adapt to the changing needs of resources when the simulation system tasks are running, thereby ensuring that the simulation system tasks can run continuously and stably. In addition, by optimizing the dynamic scheduling results of the simulation system task resources based on the dynamic change data of the real-time status of the simulation system tasks and the dynamic change data of the simulation system task resources, the allocation and scheduling of task resources can be adjusted in time to adapt to the changing needs during system operation. This real-time scheduling optimization can help to respond more flexibly to changes in the simulation system operation, ensuring that the simulation system can continue to run efficiently, thereby improving the stability and reliability of the simulation system.

[0032] Preferably, step S1 comprises the following steps:

[0033] Step S11: Acquire simulation system task resource information data;

[0034] Step S12: performing task resource configuration identification and analysis on the simulation system task resource information data to obtain simulation system task resource configuration status data;

[0035] Step S13: performing resource type statistical analysis on the simulation system task resource information data based on the simulation system task resource configuration status data to obtain simulation system task resource type data;

[0036] Step S14: performing resource characteristic connection prediction analysis on the simulation system task resource information data according to the simulation system task resource configuration status data and the simulation system task resource type data to obtain simulation system task resource characteristic connection relationship data;

[0037] Step S15: Based on the simulation system task resource characteristic connection relationship data, a knowledge graph is constructed to connect the simulation system task resource information data to obtain a simulation system task resource knowledge graph.

[0038] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0039] Step S11: Acquire simulation system task resource information data;

[0040] The embodiment of the present invention obtains necessary simulation task resource information from the simulation system by using system logs, monitoring tools or database queries, including the configuration, status, usage and other information of various task resources, to ensure the integrity and accuracy of the resource information data, and finally obtains the simulation system task resource information data.

[0041] Step S12: performing task resource configuration identification and analysis on the simulation system task resource information data to obtain simulation system task resource configuration status data;

[0042] The embodiment of the present invention analyzes the collected simulation system task resource information data by using a resource configuration identification and analysis method to identify the configuration status of various resources in the simulation system task, including determining the type, quantity, distribution and other information of the resources required for each simulation system task, and finally obtains the simulation system task resource configuration status data.

[0043] Step S13: performing resource type statistical analysis on the simulation system task resource information data based on the simulation system task resource configuration status data to obtain simulation system task resource type data;

[0044] The embodiment of the present invention performs a statistical analysis of resource types on simulation system task resource information data by combining the simulation system task resource configuration status data obtained by analysis, so as to determine the usage types of different resources in the simulation system according to the quantity, proportion and distribution of various resources in the simulation system tasks, and finally obtain simulation system task resource type data.

[0045] Step S14: performing resource characteristic connection prediction analysis on the simulation system task resource information data according to the simulation system task resource configuration status data and the simulation system task resource type data to obtain simulation system task resource characteristic connection relationship data;

[0046] The embodiment of the present invention performs a characteristic connection prediction analysis on the corresponding simulation system task resources in the simulation system task resource information data by combining the simulation system task resource configuration status data and the simulation system task resource type data obtained by analysis, so as to predict the connection relationship between different simulation system task resources, and realize the analysis and prediction of the association relationship between the resources required for the simulation system tasks, including the dependency relationship between different computing resources or the interconnection relationship between storage resources and computing resources, etc., and finally obtains the simulation system task resource characteristic connection relationship data.

[0047] Step S15: Based on the simulation system task resource characteristic connection relationship data, a knowledge graph is constructed to connect the simulation system task resource information data to obtain a simulation system task resource knowledge graph.

[0048] The embodiment of the present invention connects and constructs a knowledge graph of the knowledge relationships of corresponding simulation system task resources in the simulation system task resource information data in combination with the predicted connection relationship data of the simulation system task resource characteristics, so that the connection relationship between the simulation system task resources is represented in the form of a graph, and the correlation and dependency relationship between the resources in the simulation system tasks are more intuitively displayed, and finally a simulation system task resource knowledge graph is obtained.

[0049] The present invention first obtains necessary simulation task resource information from the simulation system, which can provide basic data for subsequent analysis and optimization, including collecting configuration, status, usage and other information of various resources from the simulation system, and providing data support for monitoring and management of the overall operation of the simulation system. By accurately obtaining task resource information data, it can provide necessary basis for subsequent knowledge graph construction, resource scheduling and optimization, thereby improving the resource scheduling efficiency and reliability of the simulation system. Secondly, by performing task resource configuration identification and analysis on the simulation system task resource information data, the configuration of various resources in the simulation system is identified, and the configuration is analyzed and sorted, providing a basis for subsequent resource management and optimization. This step involves extracting, parsing and classifying resource configuration information, thereby forming an overview of the overall resource configuration of the simulation system task. By deeply understanding the configuration status of the simulation system task resources, potential problems and bottlenecks can be discovered in time, and corresponding measures can be taken to adjust and optimize, thereby improving the performance and stability of the simulation system. Then, by performing resource type statistical analysis on the simulation system task resource information data based on the simulation system task resource configuration status data, the distribution and usage of various resources in the simulation system can be analyzed from a macro perspective, providing a reference basis for resource management and planning. Through the statistical analysis of resource types, we can understand the quantity, proportion and distribution of various resources in the simulation system tasks, and provide data support for the optimization and planning of subsequent processing processes, which helps to reasonably allocate resources, improve resource utilization, and reduce the operating cost of the simulation system, thereby providing basic data guarantee for the subsequent processing process. Next, by performing resource characteristic connection prediction analysis on the simulation system task resource information data based on the simulation system task resource configuration status data and the simulation system task resource type data, the analysis and prediction of the relationship between the resources required for the simulation system tasks can be realized, and potential resource connection patterns and laws can be discovered to provide guidance for subsequent resource optimization and configuration. This step involves mining and modeling the relationship between the resources required for the simulation system tasks, as well as predicting and analyzing the resource connection characteristics, so as to provide a basis for the reasonable allocation and optimization of system resources. By deeply understanding the connection relationship between resources, resources can be better utilized and the overall efficiency and performance of the system can be improved. Finally, by constructing the knowledge graph connection of the simulation system task resource information data based on the simulation system task resource characteristic connection relationship data, the relationship between resources can be presented in the form of a graph, thereby providing intuitive visualization support for subsequent resource management and optimization. The construction process of the knowledge graph connection involves the extraction, modeling and visualization of the relationship between resources, making the relationship between system resources clear at a glance. Through the construction of the knowledge graph, users can better understand the connection relationship between simulation task resources and discover potential optimization space, thereby providing data support for the subsequent resource scheduling process.

[0050] Preferably, step S14 comprises the following steps:

[0051] Step S141: performing task execution time prediction analysis on the simulation system task resource information data according to the simulation system task resource configuration status data to obtain simulation system task execution time prediction data;

[0052] Step S142: performing resource utilization evaluation and analysis on the simulation system task resource information data according to the simulation system task resource configuration status data and the simulation system task resource type data, to obtain the simulation system task resource type utilization data;

[0053] Step S143: performing task resource association mining analysis on the simulation system task resource information data based on the simulation system task execution time prediction data and the simulation system task resource type utilization rate data to obtain simulation system task resource association relationship data;

[0054] Step S144: constructing a task resource association network for the simulation system task resource information data based on the simulation system task resource association relationship data to obtain a simulation system task resource association connection network;

[0055] Step S145: performing resource characteristic connection prediction analysis on the simulation system task resource information data according to the simulation system task resource associated connection network to obtain simulation system task resource characteristic connection relationship data.

[0056] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S14 in the embodiment, step S14 includes the following steps:

[0057] Step S141: performing task execution time prediction analysis on the simulation system task resource information data according to the simulation system task resource configuration status data to obtain simulation system task execution time prediction data;

[0058] The embodiment of the present invention analyzes the corresponding simulation system task resources in the simulation system task resource information data according to the simulation system task resource configuration status data, so as to analyze the configuration status of the resources required for the simulation system task and the scheduling and coordination relationship between the resources, and predict the execution time of the task, so as to predict and infer the time required for the task execution, and finally obtain the simulation system task execution time prediction data.

[0059] Step S142: performing resource utilization evaluation and analysis on the simulation system task resource information data according to the simulation system task resource configuration status data and the simulation system task resource type data, to obtain the simulation system task resource type utilization data;

[0060] The embodiment of the present invention combines the simulation system task resource configuration status data and the simulation system task resource type data obtained by analysis to evaluate and analyze the utilization of the corresponding simulation system task resources in the simulation system task resource information data, so as to evaluate and determine the utilization rate of each type of resources in the simulation system task, that is, the degree to which each type of resources is effectively utilized, and finally obtains the simulation system task resource type utilization data.

[0061] Step S143: performing task resource association mining analysis on the simulation system task resource information data based on the simulation system task execution time prediction data and the simulation system task resource type utilization rate data to obtain simulation system task resource association relationship data;

[0062] The embodiment of the present invention uses an association rule mining algorithm to analyze the corresponding simulation system task resources in the simulation system task resource information data by combining the simulation system task execution time prediction data and the simulation system task resource type utilization data obtained by analysis, so as to mine the association relationship between tasks and resources from the perspective of simulation system task execution time and resource utilization, and finally obtain simulation system task resource association relationship data.

[0063] Step S144: constructing a task resource association network for the simulation system task resource information data based on the simulation system task resource association relationship data to obtain a simulation system task resource association connection network;

[0064] The embodiment of the present invention constructs a network connection for the corresponding simulation system task resources in the simulation system task resource information data by combining the dependency and influence association relationship between the simulation system tasks and resources in the simulation system task resource association relationship data obtained by analysis, and represents the association relationship between the tasks and resources in the form of a network, thereby forming a task resource association connection network, and finally obtaining a simulation system task resource association connection network.

[0065] Step S145: performing resource characteristic connection prediction analysis on the simulation system task resource information data according to the simulation system task resource associated connection network to obtain simulation system task resource characteristic connection relationship data.

[0066] The embodiment of the present invention performs characteristic connection prediction analysis on the corresponding simulation system task resources in the simulation system task resource information data in combination with the constructed simulation system task resource associated connection network, so as to predict the connection relationship between different simulation system task resources, such as the dependency relationship between resources or the association relationship between resource utilization and task execution time, and finally obtains the simulation system task resource characteristic connection relationship data.

[0067] The present invention firstly predicts and analyzes the task execution time of the simulation system task resource information data according to the simulation system task resource configuration status data, so as to predict the task execution time in combination with the use of resource configuration in the simulation system task execution process. This step can model and analyze the relationship between resource configuration and task execution time, so as to predict the time of task execution, thereby helping the subsequent processing process planning and scheduling tasks, so as to improve the efficiency and performance of subsequent resource scheduling processing. By accurately predicting the task execution time, resources can be better arranged, and the waste of resources and the idleness of the system can be avoided, so as to improve the overall utilization rate and performance of the system. Secondly, by evaluating and analyzing the resource utilization rate according to the simulation system task resource configuration status data and the simulation system task resource type data, the configuration and use of various resources in the system are comprehensively considered, and the utilization rate of resources is evaluated and analyzed, so as to provide a basis for the reasonable configuration and optimization of resources. This step associates the configuration information of the resource type with the resource utilization rate, understands the use of various resources, and provides data support for the reasonable allocation and optimization of resources. By evaluating the utilization rate of resources, the bottleneck and deficiency of resources can be discovered in time, and corresponding measures can be taken to optimize, so as to provide basic data guarantee for the subsequent processing process. Then, by performing task resource association mining and analysis on the task resource information data of the simulation system based on the task execution time prediction data of the simulation system and the task resource type utilization data of the simulation system, the association relationship between task resources can be mined from the perspective of task execution time and resource utilization, providing guidance for resource scheduling and task allocation in the subsequent processing process. This step can combine the task execution time prediction data and resource utilization data, discover the potential association relationship between tasks and resources through data mining technology, and provide decision support for resource management and task scheduling. By analyzing the association relationship between tasks and resources, resources can be allocated more reasonably, thereby providing data support for the subsequent association network construction process. Next, by constructing the task resource association network of the simulation system task resource information data based on the task resource association relationship data of the simulation system, the association relationship between tasks and resources can be presented in the form of a network, and users can be helped to intuitively understand the connection between tasks and resources in the system, providing visual support for the resource scheduling and task allocation of the system. This step can convert the association relationship between tasks and resources into a network structure and present it in the form of a network diagram, making the association relationship between tasks and resources in the system clear at a glance. By building a network of associations between tasks and resources, users can better understand the operation of the system and provide a decision-making basis for the system's resource management and task scheduling. Finally, by performing resource characteristic connection prediction analysis on the simulation system's task resource information data based on the simulation system's task resource association connection network, the potential connection patterns and rules between tasks and resources can be discovered, providing guidance for the system's resource optimization and task scheduling.This step analyzes and predicts the associated connection network between tasks and resources, explores the connection characteristics between tasks and resources, and provides optimization directions for system resource management and task scheduling. By predicting the connection relationship between tasks and resources, resources can be better planned and tasks can be scheduled, thus providing a basis for the reasonable allocation and optimization of system resources.

[0068] Preferably, step S144 includes the following steps:

[0069] Extracting task nodes and resource nodes from the task resource information data of the simulation system to obtain the task nodes of the simulation system and the task resource nodes of the simulation system;

[0070] The embodiment of the present invention processes the simulation system task resource information data by using the corresponding data extraction method to extract the tasks and resources involved in the simulation system task resource information data respectively and mark the data nodes, so that the task nodes represent the tasks in the simulation system, and the resource nodes represent the resources available for the tasks in the simulation system, such as CPU, memory, etc., and finally obtain the simulation system task nodes and simulation system task resource nodes.

[0071] Preferably, random sampling is performed on the simulation system task nodes and the simulation system task resource nodes to obtain the simulation system task random sampling points and the simulation system task resource random sampling points;

[0072] The embodiment of the present invention processes the extracted simulation system task nodes and simulation system task resource nodes by using a random sampling method to randomly select a portion of representative task node and resource node samples therefrom, so as to perform more effective processing and analysis in the subsequent analysis and processing process, and finally obtain the simulation system task random sampling points and the simulation system task resource random sampling points.

[0073] Preferably, a potential impact degree evaluation and analysis is performed on the random sampling points of the simulation system tasks and the random sampling points of the simulation system task resources to obtain a potential impact degree coefficient between the simulation system task nodes and the resource nodes;

[0074] The embodiment of the present invention analyzes the random sampling points of the simulation system tasks and the random sampling points of the simulation system task resources by using an impact assessment algorithm to evaluate the potential impact degree between the task nodes and the resource nodes, that is, the degree of association or mutual influence between them, and finally obtains the potential impact degree coefficient between the simulation system task nodes and the resource nodes.

[0075] Preferably, the potential impact relationship mining analysis is performed on the simulation system task nodes and the simulation system task resource nodes according to the potential impact degree coefficient between the simulation system task nodes and the resource nodes to obtain the simulation system task resource potential impact connection relationship data;

[0076] The embodiment of the present invention mines and analyzes the influence relationship between the simulation system task nodes and the simulation system task resource nodes by combining the potential influence degree coefficient between the simulation system task nodes and the resource nodes obtained by evaluation analysis, so as to discover the potential influence relationship between the task nodes and the resource nodes, that is, the interaction or dependency relationship between them, and finally obtain the potential influence connection relationship data of the simulation system task resources.

[0077] Preferably, a task resource association network is constructed for the simulation system task nodes and the simulation system task resource nodes based on the simulation system task resource association relationship data and the simulation system task resource potential impact connection relationship data to obtain a simulation system task resource association connection network.

[0078] The embodiment of the present invention constructs a connection network for the simulation system task nodes and the simulation system task resource nodes by combining the simulation system task resource association relationship data and the simulation system task resource potential impact connection relationship data obtained by analysis, so as to establish an association connection network between the task nodes and the resource nodes to show the relationship and interaction between them, and enable a more intuitive understanding of the connection relationship between tasks and resources in the simulation system, and finally obtain a simulation system task resource association connection network.

[0079] The present invention firstly extracts the task nodes and resource nodes of the simulation system task resource information data to realize the extraction of the task and resource nodes in the simulation system, so that the tasks and resources involved in the simulation system can be extracted respectively, thereby providing basic data for subsequent analysis and modeling. This step can simplify and refine the complex information in the simulation system, classify and summarize the tasks and resources, and lay a foundation for the subsequent steps. Secondly, by performing random sampling processing on the simulation system task nodes and the simulation system task resource nodes, sample data is extracted from the task nodes and resource nodes by random sampling to represent the task and resource situation in the simulation system, and provide representative data samples for subsequent analysis and modeling. This step obtains sample data by random sampling, which can better reflect the overall situation of tasks and resources in the system and reduce the analysis error caused by data deviation. Then, the potential impact degree evaluation and analysis is performed on the random sampling points of the simulation system tasks and the random sampling points of the simulation system task resources to evaluate the potential impact degree between them, thereby revealing the degree of association between tasks and resources and providing a basis for subsequent association mining. This step evaluates and analyzes the sample data to determine the potential impact degree coefficient between the task nodes and the resource nodes, providing a basis for the association mining in the subsequent steps. Next, the potential impact relationship mining and analysis is performed on the simulation system task nodes and the simulation system task resource nodes according to the potential impact degree coefficient between the simulation system task nodes and the resource nodes to discover the potential impact relationship between them, providing a deep understanding of the association between tasks and resources in the system. This step reveals a deeper level of association between task nodes and resource nodes by analyzing the potential impact relationship, thereby providing more comprehensive information for the system's task resource management and scheduling. Finally, by constructing a task resource association network for the simulation system task nodes and the simulation system task resource nodes based on the simulation system task resource association relationship data and the simulation system task resource potential impact connection relationship data, the association relationship between the task nodes and the resource nodes can be modeled through the network structure, thereby forming a task resource association connection network to intuitively display the association between tasks and resources, thereby providing intuitive visual support for the task scheduling and resource management of the simulation system. This step, by constructing a task resource association connection network, can present the association relationship between the task nodes and the resource nodes in the form of a network diagram, thereby providing an intuitive reference basis for the resource management and task scheduling of the simulation system, which is helpful for optimizing and improving the system operation.

[0080] Preferably, step S15 comprises the following steps:

[0081] Step S151: performing resource characteristic entity recognition analysis on the simulation system task resource information data to obtain a simulation system task resource characteristic entity data set;

[0082] The embodiment of the present invention analyzes the simulation system task resource information data by using an entity recognition method to identify and extract entities representing resource characteristics, including but not limited to task type, resource type, attributes, etc., and finally obtains a simulation system task resource characteristic entity data set.

[0083] Step S152: performing resource attribute mining and analysis on the simulation system task resource information data based on the simulation system task resource characteristic entity data set to obtain a simulation system task resource attribute data set;

[0084] The embodiment of the present invention performs attribute mining and analysis on the corresponding simulation system task resources in the simulation system task resource information data by combining the simulation system task resource characteristic entity data set obtained by analysis, so as to deeply mine the attribute information of the simulation system resources, including the performance characteristics, technical specifications, usage constraints, capacity, speed, etc. of the resources, and finally obtain the simulation system task resource attribute data set.

[0085] Step S153: extracting resource knowledge relationships from the simulation system task resource characteristic entity data set and the simulation system task resource attribute data set based on the simulation system task resource characteristic connection relationship data to obtain simulation system task resource characteristic knowledge relationship data;

[0086] The embodiment of the present invention extracts and processes the knowledge relationship between the simulation system task resource characteristic entity data set and the simulation system task resource attribute data set by combining the simulation system task resource characteristic connection relationship data obtained by analysis, so as to extract the knowledge relationship between various resource entities and attributes from the resource data, such as dependency relationship, influence relationship, etc., so as to establish the connection between various simulation system task resources and finally obtain the simulation system task resource characteristic knowledge relationship data.

[0087] Step S154: construct a knowledge graph by connecting the simulation system task resource characteristic entity data set and the simulation system task resource attribute data set according to the simulation system task resource characteristic knowledge relationship data to obtain a simulation system task resource knowledge graph.

[0088] The embodiment of the present invention connects and constructs a knowledge graph of the corresponding simulation system task resource characteristic entity data set and the simulation system task resource attribute data set by combining the simulation system task resource characteristic knowledge relationship data obtained by analysis, so as to represent the resource characteristic entities and their attributes as well as the relationship between them in a graphical form, and finally obtain a simulation system task resource knowledge graph.

[0089] The present invention firstly performs resource characteristic entity recognition analysis on the task resource information data of the simulation system, and can accurately identify characteristic entities of various resources from the task resource information data of the simulation system, including but not limited to task type, resource type, attribute, etc., thereby forming a comprehensive task resource characteristic entity data set of the simulation system. The key of this step is that it can provide an accurate entity recognition basis for subsequent resource attribute mining and knowledge relationship extraction, thereby providing accurate data support for resource management and scheduling of the simulation system. Secondly, by performing resource attribute mining and analysis on the task resource information data of the simulation system based on the task resource characteristic entity data set of the simulation system, it aims to deeply mine various attribute information hidden in the task resource information data of the simulation system, including the performance characteristics, technical specifications, usage constraints, etc. of the resources, thereby forming a comprehensive task resource attribute data set of the simulation system. The key of this step is that it can comprehensively mine and summarize the characteristics and attributes of the resources in the simulation system, and provide a sufficient data basis for the subsequent knowledge relationship extraction and knowledge graph construction. Then, by extracting resource knowledge relationships from the entity dataset of task resource characteristics of the simulation system and the attribute dataset of task resource characteristics of the simulation system based on the connection relationship data of the task resource characteristics of the simulation system, the potential associations and interactions between resources can be discovered, thereby forming a complete knowledge relationship dataset of task resource characteristics of the simulation system. The key to this step is to mine the associations between resources, reveal the mutual influence and dependency between resources, and provide in-depth understanding and support for the optimal resource configuration and task scheduling of the system. Finally, by connecting and constructing the knowledge graph of the entity dataset of task resource characteristics of the simulation system and the attribute dataset of task resource characteristics of the simulation system according to the knowledge relationship data of the task resource characteristics of the simulation system, a complete knowledge graph of task resource of the simulation system is formed, which can intuitively display the relationship network and attribute characteristics between resources, and provide an important reference basis for the intelligent decision-making and optimization of the system. The key to this step is to be able to present the characteristics and knowledge relationships of resources in the form of a graph, thereby providing intuitive and effective support for the resource management and task planning of the simulation system.

[0090] Preferably, step S2 comprises the following steps:

[0091] Step S21: performing task resource demand pattern recognition analysis on the task resource information data of the simulation system according to the task resource knowledge graph of the simulation system to obtain task resource demand pattern data of the simulation system;

[0092] The embodiment of the present invention uses a pattern recognition analysis method in combination with the constructed simulation system task resource knowledge graph to analyze and identify the corresponding simulation system task resources in the simulation system task resource information data, so as to identify and analyze the patterns and rules between the task resources, including the mutual dependence and demand patterns between the resources, and finally obtain the simulation system task resource demand pattern data.

[0093] Step S22: performing resource demand forecasting analysis on the simulation system task resource information data based on the simulation system task resource demand pattern data to obtain the simulation system task resource demand information data;

[0094] The embodiment of the present invention performs a predictive analysis on the resource requirements of the corresponding simulation system task resources in the simulation system task resource information data by combining the simulation system task resource requirement pattern data obtained by analysis, so as to predict the future resource requirements of the corresponding simulation system tasks, including computing resources and storage resources, etc., and enable it to respond to changes and fluctuations in resource requirements in advance, and finally obtain simulation system task resource requirement information data.

[0095] Step S23: performing computing resource status monitoring on the simulation system task resource requirement information data to obtain the simulation system task computing resource requirement status data;

[0096] The embodiment of the present invention monitors and analyzes the usage of computing resources in the simulation system task resource demand information data in real time, extracts the demand status data of computing resources, including but not limited to the utilization rate and load conditions of computing resources, and finally obtains the simulation system task computing resource demand status data.

[0097] Step S24: monitoring the storage resource status of the simulation system task resource requirement information data to obtain the simulation system task storage resource requirement status data;

[0098] The embodiment of the present invention monitors the usage and status of storage resources in the simulation system in real time by performing real-time monitoring and analysis on the usage of storage resources in the simulation system task resource demand information data, and extracts the storage resource demand status data therefrom, including but not limited to the utilization rate of storage resources, available space, etc., and finally obtains the simulation system task storage resource demand status data.

[0099] Step S25: merging the simulation system task computing resource requirement status data and the simulation system task storage resource requirement status data to obtain simulation system task resource status information data.

[0100] The embodiment of the present invention merges the monitored simulation system task computing resource demand status data and the simulation system task storage resource demand status data through a data merging method to integrate the demand status information of different types of resources, and more comprehensively understand the usage and demand trends of the simulation system task resources, and finally obtain the simulation system task resource status information data.

[0101] The present invention firstly performs task resource demand pattern recognition analysis on the task resource information data of the simulation system according to the task resource knowledge graph of the simulation system. This step can identify and analyze the patterns and rules between task resources, including the mutual dependence and demand patterns between resources, and then form a comprehensive simulation system task resource demand pattern data set. This data set is the result of pattern recognition based on actual data, and can provide reliable data support for subsequent resource management and planning. By deeply exploring the internal connection between task resources, the simulation system's understanding and prediction capabilities of resource demand can be improved, providing an important reference basis for the reasonable allocation and scheduling of resources, thereby improving the resource scheduling efficiency and performance of the simulation system. Secondly, by predicting and analyzing the resource demand of the simulation system task resource information data based on the simulation system task resource demand pattern data, in this step, it is possible to predict and analyze the future resource demand based on the existing simulation system task resource demand pattern data and use the prediction analysis technology to form a comprehensive simulation system task resource demand information data set. This data set is based on the prediction analysis of historical data and demand pattern recognition results, which provides an important reference for the system's resource planning and scheduling, enabling the system to respond to changes and fluctuations in resource demand in advance, thereby improving the system's flexibility and responsiveness. The key to this step is that it can provide the simulation system with the ability to predict and analyze resource demand, enabling the system to manage and schedule resources more intelligently, thereby providing basic data guarantee for subsequent processing. Then, by monitoring the computing resource status of the simulation system task resource demand information data, the computing resource demand status data is extracted from it, including but not limited to the utilization rate and load of computing resources. This data set is based on real-time monitoring and analysis of resource demand information, and can provide real-time data support for the system's computing resource management and optimization. The key to this step is that the simulation system can monitor and understand the status of computing resources in real time, and adjust the resource allocation strategy in time, thereby ensuring the stable operation and efficient performance of the simulation system. Next, the storage resource status is monitored on the simulation system task resource demand information data to extract the storage resource demand status data, including but not limited to storage resource utilization, available space, etc. This data set is based on real-time monitoring and analysis of resource demand information, and provides real-time data support for the system's storage resource management and optimization. The key to this step is to enable the system to monitor and understand the status of storage resources in real time, and adjust the resource allocation strategy in time, thereby providing data support for the subsequent data merging process.Finally, by merging the simulation system task computing resource demand status data and the simulation system task storage resource demand status data, a comprehensive simulation system task resource status information data set is formed. This data set integrates the demand status information of computing resources and storage resources, thereby providing comprehensive data support for subsequent resource management and scheduling. The key to this step is that it can provide a comprehensive understanding of the simulation system resource status, allowing the simulation system to schedule and manage resources more intelligently, thereby improving the efficiency and performance of the subsequent scheduling process.

[0102] Preferably, step S3 comprises the following steps:

[0103] Step S31: performing task completion prediction analysis based on the simulation system task resource status information data to obtain simulation system task completion status information data;

[0104] The embodiment of the present invention uses a corresponding predictive analysis algorithm to analyze the simulation system task resource status information data obtained by analysis to predict factors such as the type of task in the simulation system, resource requirements, and the current load conditions of the system, and predicts the completion status of the corresponding simulation system task in combination with the relevant factors obtained by analysis, and finally obtains the simulation system task completion status information data.

[0105] Step S32: obtaining the completion time and delay time of each simulation system task through the simulation system task completion information data, and performing difference average analysis according to the completion time and delay time of each simulation system task to obtain the average waiting time for the simulation system task completion;

[0106] The embodiment of the present invention obtains the completion time and delay time of each simulation system task by using a statistical method based on the time of the simulation system task completion information data obtained by analysis, and performs difference calculation on the completion time and delay time of each simulation system task by using a difference calculation method and accumulates the sum to statistically obtain the waiting time for the completion of the corresponding simulation system task, and at the same time performs arithmetic average calculation on the statistically obtained waiting time to finally obtain the average waiting time for the completion of the simulation system task.

[0107] Step S33: performing task priority statistical analysis on the simulation system task resource status information data based on the simulation system task completion status information data and the average waiting time for the simulation system task completion to generate a simulation system task resource requirement priority sequence;

[0108] The embodiment of the present invention performs a statistical analysis of the priority of the simulation system task resource status information data by combining the simulation system task completion information data and the average waiting time for the simulation system task completion, so as to determine the priority sorting of resources within each simulation system task by comprehensively considering the urgency of the simulation system task and the degree of impact on the simulation system. This sequence will determine the priority of the simulation system tasks in resource allocation, ensure that the system task resources are used in the most reasonable way, so as to meet the urgency and importance requirements of the tasks, and finally generate a simulation system task resource requirement priority sequence.

[0109] Step S34: dynamically matching and scheduling the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task resource dynamic scheduling result.

[0110] The embodiment of the present invention performs resource matching analysis on the corresponding simulation system tasks in the simulation system task resource requirement information data obtained by combining the resource requirement status corresponding to the corresponding simulation system tasks in the simulation system task resource requirement information data, so as to effectively allocate system task resources by fully considering the importance, urgency and resource requirement of the simulation system tasks, and uses intelligent scheduling algorithms and technologies to design the optimal resource scheduling strategy for different priorities and resource requirements of simulation system tasks according to the resource matching results obtained after matching. At the same time, the corresponding simulation system tasks in the simulation system task resource requirement priority sequence are subjected to real-time resource scheduling processing according to the designed simulation system task resource intelligent scheduling strategy, so as to dynamically adjust the resource allocation of the simulation system tasks and meet the real-time requirements of the simulation system task operation, and finally obtain the dynamic scheduling result of the simulation system task resources.

[0111] The present invention firstly predicts and analyzes the task completion according to the task resource status information data of the simulation system, so as to predict and analyze the usage of the task resources in the simulation system, historical data and other related factors, so as to predict the completion of the corresponding simulation task, which helps to understand the execution of the current simulation task, so as to find possible problems in advance and make reasonable arrangements for subsequent tasks. By predicting and analyzing the completion information data of the simulation system task, it is possible to provide an expectation of the task completion time, which helps to optimize resource utilization and task scheduling. Secondly, by obtaining the completion time and delay time of each simulation system task according to the simulation system task completion information data, further, by performing a difference average analysis according to the completion time and delay time of each simulation system task, the average waiting time for the completion of the simulation system task can be obtained. This analysis and processing process helps to evaluate the efficiency and accuracy of task execution, and can also reveal the possible bottlenecks or optimization space in the system, providing data support for the subsequent task demand priority sorting process. Then, by performing task priority statistical analysis on the task resource status information data of the simulation system based on the task completion information data of the simulation system and the average waiting time for the task completion of the simulation system, it means that the simulation system can sort and adjust the priority of its simulation task resource requirements according to the task completion status and waiting time. Through this analysis, resources can be allocated more effectively, task execution efficiency can be improved, task delay can be reduced, and system requirements can be met to the greatest extent. Finally, by performing resource dynamic matching scheduling on the simulation system task resource requirement priority sequence based on the task resource requirement information data of the simulation system, this process is to dynamically adjust resource allocation according to the current resource status of the simulation system and the priority of the task, so as to meet the requirements of the system tasks to the greatest extent. Through dynamic resource matching scheduling, it is possible to respond more flexibly to changes in the requirements of different tasks, improve resource utilization, optimize system performance, and ensure the smooth execution of tasks.

[0112] Preferably, step S33 includes the following steps:

[0113] Step S331: performing task emergency completion impact assessment analysis based on the simulation system task completion status information data and the average waiting time for the simulation system task completion to obtain the simulation system task emergency completion impact assessment index;

[0114] The embodiment of the present invention evaluates and analyzes the impact of urgent completion of corresponding simulation system tasks by combining the simulation system task completion information data and the average waiting time for simulation system task completion obtained by analysis, so as to evaluate and determine which evaluation indicators have a higher impact on the simulation system tasks, and help the simulation system evaluate and determine which tasks have a higher urgency, and finally obtain the simulation system task urgent completion impact evaluation indicators.

[0115] Step S332: performing task resource utilization evaluation and analysis on the simulation system task resource status information data based on the simulation system task completion status information data to obtain a simulation system task resource utilization evaluation index;

[0116] The embodiment of the present invention evaluates and analyzes the utilization of task resources in the simulation system task resource status information data by combining the simulation system task completion information data obtained by analysis, so as to determine the utilization of task resources and understand the utilization efficiency of the simulation system task resources, including the utilization of CPU, memory, network and other resources, and finally obtain the simulation system task resource utilization evaluation index.

[0117] Step S333: performing task resource dependency evaluation and analysis on the simulation system task resource status information data based on the simulation system task completion status information data to obtain an evaluation index of the dependency relationship between the simulation system task resources;

[0118] The embodiment of the present invention evaluates and analyzes the dependency relationship between resources required for corresponding simulation system tasks in the simulation system task resource status information data by combining the simulation system task completion status information data obtained by analysis, so as to determine the dependency relationship between task resources, including the mutual dependency between task resources and the dependency on shared resources, and evaluates and determines the indicator parameters related to the dependency relationship between task resources, and finally obtains the dependency evaluation indicator between simulation system task resources.

[0119] Step S334: merging the simulation system task emergency completion impact assessment index, the simulation system task resource utilization assessment index, and the simulation system task resource dependency assessment index to obtain a simulation system task resource assessment index set; performing indicator weight allocation processing on the simulation system task resource assessment index set to obtain a simulation system task resource assessment index weight parameter;

[0120] The embodiment of the present invention combines the simulation system task emergency completion impact evaluation index, the simulation system task resource utilization evaluation index and the simulation system task resource dependency evaluation index obtained by the previous evaluation analysis, thereby obtaining a simulation system task resource evaluation index set. Then, by comprehensively considering the urgency of the task, the utilization of resources and the dependency between tasks and resources, the importance of the corresponding indicators in the simulation system task resource evaluation index set is weighted to determine the importance of each indicator, and finally obtain the simulation system task resource evaluation index weight parameter.

[0121] Step S335: performing a priority comprehensive evaluation and analysis on the simulation system task resource evaluation indicator set based on the simulation system task resource evaluation indicator weight parameter to obtain a simulation system task resource priority evaluation score;

[0122] The embodiment of the present invention accumulates and sums the corresponding resource evaluation indicators in the simulation system task resource evaluation indicator set by combining the allocated simulation system task resource evaluation indicator weight parameters, so as to quantitatively determine the priority score of each task resource by comprehensively considering the weights of various indicators, and finally obtain the simulation system task resource priority evaluation score.

[0123] Step S336: Perform task priority statistical analysis on the simulation system task resource status information data according to the simulation system task resource priority evaluation score to generate a simulation system task resource requirement priority sequence.

[0124] The embodiment of the present invention prioritizes and statistics the corresponding simulation system task resources in the simulation system task resource status information data by combining the simulation system task resource priority evaluation scores obtained through analysis, so as to generate a corresponding task resource requirement priority sequence according to the task priority situation. This sequence will determine the priority of the simulation system tasks in resource allocation, ensure that the system task resources are used in the most reasonable way, so as to meet the urgency and importance requirements of the tasks, and finally generate a simulation system task resource requirement priority sequence.

[0125] The present invention firstly performs task emergency completion impact assessment analysis based on simulation system task completion information data and simulation system task completion average waiting time, and can objectively assess the urgency of tasks and convert it into an emergency completion impact assessment index. This assessment index can help the simulation system determine which tasks have higher urgency, so as to give priority to these tasks during resource allocation and scheduling, so as to meet the emergency needs of the system to the greatest extent and improve the overall operation efficiency. Secondly, by performing task resource utilization assessment analysis on simulation system task resource status information data based on simulation system task completion information data, the task completion status and resource status can be comprehensively considered, so as to obtain a task resource utilization rate assessment index. This index can reflect the effective utilization degree of simulation system resources, help system managers understand the resource allocation situation, and then adjust the resource allocation strategy, improve resource utilization efficiency, and ensure that the simulation system can give full play to its potential. Then, by performing task resource dependency assessment analysis on simulation system task resource status information data based on simulation system task completion information data, the dependency relationship between tasks and resources can be assessed, and it can be converted into a task resource dependency assessment index. This index can help understand the mutual influence between tasks and resources, so as to better plan resource allocation and scheduling strategies, and ensure that tasks can be successfully completed and resources are reasonably utilized. At the same time, by merging the task emergency completion impact evaluation index, task resource utilization evaluation index and task resource dependency evaluation index, the urgency of the task, the utilization of resources and the dependency between tasks and resources can be comprehensively considered, thereby obtaining a comprehensive evaluation index set. By weighting the comprehensive evaluation index set, the importance of each indicator can be determined, providing a basis for the subsequent priority comprehensive evaluation. Next, by performing a priority comprehensive evaluation analysis on the simulation system task resource evaluation index set based on the simulation system task resource evaluation index weight parameter, the importance of each indicator can be comprehensively considered, thereby quantitatively evaluating and obtaining the task resource priority evaluation score. This evaluation score can help better understand the priority of tasks and resources, thereby optimizing resource allocation and scheduling strategies, and ensuring that the simulation system tasks can run efficiently and stably. Finally, by performing task priority statistical analysis on the simulation system task resource status information data according to the simulation system task resource priority evaluation score, a task resource demand priority sequence can be generated according to the priority of the task. This sequence can help reasonably arrange the execution order of tasks and give priority to tasks with higher importance, thereby improving the operation efficiency and response speed of the simulation system and meeting user needs.

[0126] Preferably, step S34 includes the following steps:

[0127] Step S341: performing resource matching analysis on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task priority sequence resource matching result;

[0128] The embodiment of the present invention performs resource matching analysis on the corresponding simulation system tasks in the simulation system task resource requirement information data obtained by combining the resource requirement status corresponding to the corresponding simulation system tasks in the simulation system task resource requirement information data, so as to fully consider the importance, urgency and resource requirement of the simulation system tasks, effectively allocate system task resources, and ensure that the simulation system tasks in the high priority sequence can be met first, and finally obtain the simulation system task priority sequence resource matching result.

[0129] Step S342: Perform intelligent scheduling design analysis based on the simulation system task priority sequence resource matching result to obtain the simulation system task resource intelligent scheduling strategy;

[0130] The embodiment of the present invention uses intelligent scheduling algorithms and technologies to design the optimal resource scheduling strategy for the priorities and resource requirements of different simulation system tasks based on the simulation system task priority sequence resource matching results obtained after matching, so as to maximize the efficient management and utilization of resources by the simulation system tasks, and finally obtain the simulation system task resource intelligent scheduling strategy.

[0131] Step S343: performing real-time resource scheduling processing on the simulation system task resource requirement priority sequence according to the simulation system task resource intelligent scheduling strategy to obtain the simulation system task resource dynamic scheduling result.

[0132] The embodiment of the present invention performs real-time resource scheduling processing on the simulation system tasks corresponding to the simulation system task resource requirement priority sequence according to the designed simulation system task resource intelligent scheduling strategy, so as to dynamically adjust the resource allocation of the simulation system tasks and meet the real-time requirements of the simulation system task operation, so that the simulation system can respond to the demand changes of different tasks more flexibly and efficiently, and finally obtain the dynamic scheduling result of the simulation system task resources.

[0133] The present invention firstly performs resource matching analysis on the priority sequence of task resource requirements of the simulation system based on the task resource requirement information data of the simulation system. In order to effectively allocate system task resources to meet the execution requirements of the tasks, the optimal resource allocation scheme can be found by analyzing the priority sequence of the tasks and the availability of resources, thereby improving the resource utilization efficiency of the system and ensuring that high-priority tasks are met first. This analysis and processing process can help the simulation system manager to better understand the resource allocation situation and adjust the resource allocation strategy in time to meet the operation requirements of the simulation system tasks. Then, by performing intelligent scheduling design analysis according to the resource matching results of the task priority sequence of the simulation system, a more efficient task scheduling strategy can be designed according to the resource requirements of the corresponding simulation system tasks. By comprehensively considering the priority of the tasks, the availability of resources and the operation status of the system, an intelligent scheduling strategy can be designed to maximize the performance and efficiency of the system. This analysis and processing process helps to optimize the task execution order of the system, reduce the waiting time of the tasks, improve the execution speed of the tasks, and ensure that the simulation system tasks can quickly respond to the needs of users. Finally, by performing real-time resource scheduling on the priority sequence of task resource requirements of the simulation system according to the intelligent scheduling strategy of the simulation system task resources, the execution order of tasks and the allocation of resources can be adjusted dynamically to adapt to changes in the system runtime. Through real-time scheduling, the allocation of resources can be dynamically adjusted according to the current system status and task priority to ensure that the system can respond to user needs in a timely manner and optimize system performance. This process can effectively reduce the waiting time of tasks and the waste of resources, thereby improving the overall operation efficiency and stability of simulation system tasks.

[0134] Preferably, step S4 comprises the following steps:

[0135] Step S41: monitoring the dynamic changes of simulation tasks on the simulation system task resource information data to obtain the dynamic changes of the real-time status of the simulation system tasks;

[0136] The embodiment of the present invention monitors the simulation system task resource information data by using a task monitoring method to capture the dynamic changes of the simulation system tasks in real time according to the change information of its resources, including monitoring the addition, deletion, modification, etc. of simulation tasks, so as to timely understand the real-time dynamic changes of the status of the simulation system tasks, and finally obtain the real-time dynamic changes data of the status of the simulation system tasks.

[0137] Step S42: performing resource change time series prediction analysis on the simulation system task resource information data to obtain simulation system task resource state change trend data;

[0138] The embodiment of the present invention predicts and analyzes the resource changes of the simulation system task resource information data by using the time series change prediction and analysis technology to predict the changing trend of the simulation system task resource status, including the resource demand, utilization rate, remaining amount, etc., and enables it to predict the resource shortage or surplus in advance, and finally obtain the simulation system task resource status change trend data.

[0139] Step S43: monitoring the dynamic change of the task resource information data of the simulation system based on the state change trend data of the task resource of the simulation system, so as to obtain the dynamic change data of the task resource of the simulation system;

[0140] The embodiment of the present invention uses a resource change monitoring method to monitor the simulation system task resource information data by combining the simulation system task resource status change trend data obtained by analysis, so as to monitor the dynamic changes of task resources in real time, including increase, decrease, and reallocation of resources, so as to adapt to the changing needs of resources when the simulation system tasks are running, and finally obtain the dynamic change data of the simulation system task resources.

[0141] Step S44: performing resource scheduling optimization and updating according to the dynamic change data of the real-time status of the simulation system tasks and the dynamic change data of the simulation system task resources, so as to generate a simulation system task resource scheduling optimization strategy;

[0142] The embodiment of the present invention generates a corresponding resource scheduling optimization strategy by combining the real-time dynamic change data of the simulation system task status and the dynamic change data of the simulation system task resources obtained by analysis, so as to dynamically adjust the allocation and scheduling of the corresponding simulation system task resources according to the real-time task status and the dynamic change of resources, so as to maximize the performance and efficiency of the simulation system, ensure that the simulation tasks can be completed on time and the resources are reasonably utilized, and finally generate a simulation system task resource scheduling optimization strategy.

[0143] Step S45: Based on the simulation system task resource scheduling optimization strategy, the simulation system task resource dynamic scheduling result is optimized in real time to obtain the simulation system task resource real-time scheduling optimization result.

[0144] The embodiment of the present invention performs real-time scheduling optimization on the dynamic scheduling results of the simulation system task resources of the corresponding simulation tasks in combination with the simulation system task resource scheduling optimization strategy after dynamic optimization update, so as to optimize the task resources in real time according to the resource scheduling optimization strategy, so as to timely adjust the allocation and scheduling of task resources to adapt to the changing needs during the operation of the simulation system tasks, and finally obtain the real-time scheduling optimization results of the simulation system task resources.

[0145] The present invention firstly monitors the dynamic changes of simulation tasks on the task resource information data of the simulation system, and can capture the state changes of tasks in real time, including the addition, deletion, modification, etc. of tasks. This monitoring process can help the system manager to timely understand the real-time change state of the simulation system tasks, so as to make timely response and adjustment, and ensure that the simulation system can run smoothly. The acquisition of the real-time state dynamic change data can provide important data support for the subsequent resource scheduling and optimization of the system. Secondly, by performing resource change time series prediction analysis on the task resource information data of the simulation system, the change trend of the task resource state can be predicted, including the demand, utilization rate, and remaining amount of resources, which helps the system manager to better plan the allocation and scheduling of resources, predict the shortage or surplus of resources in advance, and take corresponding measures to ensure the stability and efficiency of the system. Then, by monitoring the dynamic changes of resources on the task resource information data of the simulation system based on the task resource state change trend data of the simulation system, the dynamic changes of task resources can be monitored in real time, including the increase, reduction, and reallocation of resources, and this monitoring process can help to timely adjust the resource allocation strategy to adapt to the change demand of resources when the simulation system tasks are running, so as to ensure that the simulation system tasks can run continuously and stably. Next, by optimizing and updating resource scheduling according to the dynamic change data of the real-time status of the simulation system tasks and the dynamic change data of the simulation system task resources, a more effective resource scheduling optimization strategy can be generated. This optimization processing method can dynamically adjust the allocation and scheduling of resources according to the real-time status of the system and the changes in resources to maximize the performance and efficiency of the system, thereby ensuring that tasks can be completed on time and resources are reasonably used. Finally, by performing real-time scheduling optimization on the dynamic scheduling results of the simulation system task resources based on the simulation system task resource scheduling optimization strategy, the allocation and scheduling of task resources can be adjusted in time to adapt to the changing needs of the system during operation. This real-time scheduling optimization can help to more flexibly respond to changes in the simulation system during operation, ensuring that the simulation system can continue to run efficiently, thereby improving the stability and reliability of the simulation system.

[0146] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0147] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A simulation resource scheduling method based on knowledge graph, characterized in that: The following steps are involved: Step S1: Acquire simulation system task resource information data, and perform resource characteristic connection prediction analysis on the simulation system task resource information data to obtain simulation system task resource characteristic connection relationship data; Based on the characteristic connection relationship data of the simulation system task resources, the knowledge graph of the simulation system task resource information data is connected and constructed to obtain the knowledge graph of the simulation system task resources; Step S2: performing task resource demand analysis on the simulation system task resource information data according to the simulation system task resource knowledge graph to obtain the simulation system task resource demand information data; performing resource status monitoring processing on the simulation system task resource demand information data to obtain the simulation system task resource status information data; Step S3: Perform task priority statistics analysis according to the simulation system task resource status information data to generate a simulation system task resource requirement priority sequence; perform resource dynamic matching scheduling on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task resource dynamic scheduling result; wherein step S3 includes the following steps: Step S31: performing task completion prediction analysis based on the simulation system task resource status information data to obtain simulation system task completion status information data; Step S32: obtaining the completion time and delay time of each simulation system task through the simulation system task completion information data, and performing difference average analysis according to the completion time and delay time of each simulation system task to obtain the average waiting time for the simulation system task completion; Step S33: Performing task priority statistical analysis on the simulation system task resource status information data based on the simulation system task completion information data and the average waiting time for the simulation system task completion to generate a simulation system task resource requirement priority sequence; wherein step S33 includes the following steps: Step S331: performing task emergency completion impact assessment analysis based on the simulation system task completion status information data and the average waiting time for the simulation system task completion to obtain the simulation system task emergency completion impact assessment index; Step S332: performing task resource utilization evaluation and analysis on the simulation system task resource status information data based on the simulation system task completion status information data to obtain a simulation system task resource utilization evaluation index; Step S333: performing task resource dependency evaluation and analysis on the simulation system task resource status information data based on the simulation system task completion status information data to obtain an evaluation index of the dependency relationship between the simulation system task resources; Step S334: merging the simulation system task emergency completion impact assessment index, the simulation system task resource utilization assessment index, and the simulation system task resource dependency assessment index to obtain a simulation system task resource assessment index set; performing indicator weight allocation processing on the simulation system task resource assessment index set to obtain a simulation system task resource assessment index weight parameter; Step S335: performing a priority comprehensive evaluation and analysis on the simulation system task resource evaluation indicator set based on the simulation system task resource evaluation indicator weight parameter to obtain a simulation system task resource priority evaluation score; Step S336: performing task priority statistical analysis on the simulation system task resource status information data according to the simulation system task resource priority evaluation score to generate a simulation system task resource requirement priority sequence; Step S34: performing dynamic resource matching and scheduling on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task resource dynamic scheduling result; Step S4: monitoring the dynamic changes of simulation tasks and resources on the simulation system task resource information data to obtain the dynamic change data of the real-time status of the simulation system tasks and the dynamic change data of the simulation system task resources; performing real-time scheduling optimization on the dynamic scheduling results of the simulation system task resources based on the real-time dynamic change data of the simulation system tasks and the dynamic change data of the simulation system task resources to obtain the real-time scheduling optimization results of the simulation system task resources; wherein step S4 includes the following steps: Step S41: monitoring the dynamic changes of simulation tasks on the simulation system task resource information data to obtain the dynamic changes of the real-time status of the simulation system tasks; Step S42: performing resource change time series prediction analysis on the simulation system task resource information data to obtain simulation system task resource state change trend data; Step S43: monitoring the dynamic change of the task resource information data of the simulation system based on the state change trend data of the task resource of the simulation system, so as to obtain the dynamic change data of the task resource of the simulation system; Step S44: performing resource scheduling optimization and updating according to the dynamic change data of the real-time status of the simulation system tasks and the dynamic change data of the simulation system task resources, so as to generate a simulation system task resource scheduling optimization strategy; Step S45: Based on the simulation system task resource scheduling optimization strategy, the simulation system task resource dynamic scheduling result is optimized in real time to obtain the simulation system task resource real-time scheduling optimization result.

2. The simulation resource scheduling method based on knowledge graph according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire simulation system task resource information data; Step S12: performing task resource configuration identification and analysis on the simulation system task resource information data to obtain simulation system task resource configuration status data; Step S13: performing resource type statistical analysis on the simulation system task resource information data based on the simulation system task resource configuration status data to obtain simulation system task resource type data; Step S14: performing resource characteristic connection prediction analysis on the simulation system task resource information data according to the simulation system task resource configuration status data and the simulation system task resource type data to obtain simulation system task resource characteristic connection relationship data; Step S15: Based on the simulation system task resource characteristic connection relationship data, a knowledge graph is constructed to connect the simulation system task resource information data to obtain a simulation system task resource knowledge graph.

3. The simulation resource scheduling method based on knowledge graph according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: performing task execution time prediction analysis on the simulation system task resource information data according to the simulation system task resource configuration status data to obtain simulation system task execution time prediction data; Step S142: performing resource utilization evaluation and analysis on the simulation system task resource information data according to the simulation system task resource configuration status data and the simulation system task resource type data, to obtain the simulation system task resource type utilization data; Step S143: performing task resource association mining analysis on the simulation system task resource information data based on the simulation system task execution time prediction data and the simulation system task resource type utilization rate data to obtain simulation system task resource association relationship data; Step S144: constructing a task resource association network for the simulation system task resource information data based on the simulation system task resource association relationship data to obtain a simulation system task resource association connection network; Step S145: performing resource characteristic connection prediction analysis on the simulation system task resource information data according to the simulation system task resource associated connection network to obtain simulation system task resource characteristic connection relationship data.

4. The simulation resource scheduling method based on knowledge graph according to claim 3 is characterized in that: Step S144 includes the following steps: Extracting task nodes and resource nodes from the task resource information data of the simulation system to obtain the task nodes of the simulation system and the task resource nodes of the simulation system; Performing random sampling processing on the simulation system task nodes and the simulation system task resource nodes to obtain the simulation system task random sampling points and the simulation system task resource random sampling points; Perform potential impact evaluation and analysis on the random sampling points of simulation system tasks and the random sampling points of simulation system task resources to obtain the potential impact coefficient between the simulation system task nodes and resource nodes; According to the potential influence degree coefficient between the simulation system task nodes and the resource nodes, the simulation system task nodes and the simulation system task resource nodes are mined and analyzed for potential influence relationships, and the simulation system task resource potential influence connection relationship data is obtained; Based on the simulation system task resource association relationship data and the simulation system task resource potential impact connection relationship data, a task resource association network is constructed for the simulation system task nodes and the simulation system task resource nodes to obtain a simulation system task resource association connection network.

5. The simulation resource scheduling method based on knowledge graph according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: performing resource characteristic entity recognition analysis on the simulation system task resource information data to obtain a simulation system task resource characteristic entity data set; Step S152: performing resource attribute mining and analysis on the simulation system task resource information data based on the simulation system task resource characteristic entity data set to obtain a simulation system task resource attribute data set; Step S153: extracting resource knowledge relationships from the simulation system task resource characteristic entity data set and the simulation system task resource attribute data set based on the simulation system task resource characteristic connection relationship data to obtain simulation system task resource characteristic knowledge relationship data; Step S154: construct a knowledge graph by connecting the simulation system task resource characteristic entity data set and the simulation system task resource attribute data set according to the simulation system task resource characteristic knowledge relationship data to obtain a simulation system task resource knowledge graph.

6. The method for scheduling simulation resources based on knowledge graph according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing task resource demand pattern recognition analysis on the task resource information data of the simulation system according to the task resource knowledge graph of the simulation system to obtain task resource demand pattern data of the simulation system; Step S22: performing resource demand forecasting analysis on the simulation system task resource information data based on the simulation system task resource demand pattern data to obtain the simulation system task resource demand information data; Step S23: performing computing resource status monitoring on the simulation system task resource requirement information data to obtain the simulation system task computing resource requirement status data; Step S24: monitoring the storage resource status of the simulation system task resource requirement information data to obtain the simulation system task storage resource requirement status data; Step S25: merging the simulation system task computing resource requirement status data and the simulation system task storage resource requirement status data to obtain simulation system task resource status information data.

7. The simulation resource scheduling method based on knowledge graph according to claim 1 is characterized in that: Step S34 includes the following steps: Step S341: performing resource matching analysis on the simulation system task resource requirement priority sequence based on the simulation system task resource requirement information data to obtain a simulation system task priority sequence resource matching result; Step S342: Perform intelligent scheduling design analysis based on the simulation system task priority sequence resource matching result to obtain the simulation system task resource intelligent scheduling strategy; Step S343: performing real-time resource scheduling processing on the simulation system task resource requirement priority sequence according to the simulation system task resource intelligent scheduling strategy to obtain the simulation system task resource dynamic scheduling result.

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