Construction method of visual modeling job flow scheduling engine

Abstract

The invention relates to the technical field of construction methods, and discloses a construction method of a visual modeling job flow scheduling engine, which comprises the following steps: S1, defining a job flow model: defining the job flow model including job nodes, connecting lines and job attributes, the job nodes representing each link in the job flow, the connecting lines representing each link in the job flow, and the job attributes representing each link in the job flow; the connecting line represents a logical relationship between the operation nodes; and S2, constructing a visual modeling interface: constructing the visual modeling interface according to the workflow model, and providing dragging and connecting functions, so that a user can conveniently construct the workflow. Through an intelligent scheduling algorithm, dynamic adjustment can be carried out according to real-time resource conditions and job flow characteristics, and the execution efficiency and the success rate of the job flow are improved; through reasonable resource allocation and monitoring, resources can be fully utilized, and resource consumption and waste are reduced; through the visual modeling interface, the user can conveniently construct and manage the workflow, and the user experience and satisfaction degree are improved.

Description

Technical Field

[0001] The present invention relates to the field of construction methods, and in particular to a construction method of a visual modeling workflow scheduling engine. Background Art

[0002] The flow scheduling engine is the core component responsible for managing and scheduling the job flow. It needs to reasonably allocate resources according to the various links in the job flow and the logical relationship between them to ensure that the job flow can be smoothly executed according to the predetermined order and time requirements. When building a flow scheduling engine, multiple factors need to be considered, such as the complexity of the job flow, the availability of resources, and the execution time requirements.

[0003] However, there is a problem with the construction of existing modeling workflow scheduling engines, that is, the workflow scheduling algorithm is not intelligent enough and cannot be dynamically adjusted according to real-time resource conditions and workflow characteristics. As a result, when resources are tight or the workflow is complex, the scheduling engine may not be able to make the best scheduling decision, thus affecting the execution efficiency and success rate of the workflow.

[0004] To solve the above problems, this application proposes a method for constructing a visual modeling workflow scheduling engine. Summary of the invention

[0005] 1. Purpose of the invention In order to solve the technical problems existing in the background technology, the present invention proposes a method for constructing a visual modeling workflow scheduling engine. The present invention can dynamically adjust according to the real-time resource situation and the characteristics of the workflow through an intelligent scheduling algorithm to improve the execution efficiency and success rate of the workflow; through reasonable resource allocation and monitoring, resources can be fully utilized and resource consumption and waste can be reduced; through a visual modeling interface, users can easily build and manage workflows, thereby improving user experience and satisfaction.

[0006] (II) Technical solution To solve the above problems, the present invention provides a method for constructing a visual modeling workflow scheduling engine, comprising the following steps: S1. Define the workflow model First, you need to define the job flow model, including job nodes, connecting lines, and job attributes. Job nodes represent each link in the job flow, and connecting lines represent the logical relationship between job nodes. S2. Build a visual modeling interface According to the workflow model, a visual modeling interface is constructed, providing drag and drop and connection functions, so that users can easily build workflows; S3. Design resource allocation algorithm Design a resource allocation algorithm that can dynamically allocate resources to job nodes based on current resource status and job priority factors; Algorithm formula: Ri = f(Si, Pi, Ai); Among them, Ri represents the amount of resources allocated to job node i, Si represents the resource demand of job node i, Pi represents the priority of job node i, Ai represents the current amount of available resources, and f represents the resource allocation function; S4. Design of job flow scheduling algorithm Design a job flow scheduling algorithm based on job flow characteristics and resource allocation results. This algorithm can determine the execution order and time of job nodes according to the logical relationship between job nodes and resource allocation. Algorithm formula: Ti = g(Di,Ri,Ci); Among them, Ti represents the execution time of job node i, Di represents the dependency of job node i, Ri represents the amount of resources allocated to job node i, Ci represents the computational complexity of job node i, and g represents the job flow scheduling function; S5. Implement resource monitoring and dynamic adjustment Implement resource monitoring function, obtain resource usage in real time, and dynamically adjust resource allocation and job flow scheduling strategies based on resource monitoring results; S6. Build a flow scheduling engine Integrate the above algorithms and modules into the flow scheduling engine to form a complete job flow scheduling system; S7, Testing and Optimization Test the constructed stream scheduling engine to verify its performance and stability, and optimize it based on the test results; S8. Deployment and Application Deploy the optimized flow scheduling engine to actual application scenarios to provide users with efficient and reliable job flow scheduling services.

[0007] Preferably, in S1, it specifically includes: Job node definition: Determine the type of job node: data processing node, computing node, decision node; Define the properties of a node including: node ID, name, description, input / output parameters, and execution logic; Design node status mechanisms, including pending, executing, completed, and failed; Connection line definition: Determine the type of connection line (such as sequential connection, parallel connection, conditional connection); Define the properties of the connection line, including the connection ID, start node, end node, and conditional expression.

[0008] Design the logic of the connection lines to ensure the correct dependencies between job nodes; Job property definition: Define the overall properties of the job flow, such as name, description, and version number; Define specific properties for each job node, such as resource requirements, priority, and computational complexity.

[0009] Preferably, in S2, specifically including: Interface design: Design user-friendly interface layout, including toolbar, node library, and canvas area; Implement the drag-and-drop function, allowing users to drag nodes from the node library to the canvas; Implemented the connection function, allowing users to create connection lines by clicking and dragging on the canvas; Property configuration: Provides a property configuration panel for the selected node or connection line, allowing users to modify its properties; Implement real-time preview function to show the impact of user-configured properties on the job flow; Save and Load: Implement the job flow saving function, allowing users to save the constructed job flow to a file or database; Implement the job flow loading function, allowing users to load existing job flows from files or databases.

[0010] Preferably, in S3, specifically including: Resource information collection: Collect status information of various resources in the system in real time, such as CPU usage, memory usage, and disk space; Design resource information update mechanism to ensure the timeliness and accuracy of resource information; Resource Needs Assessment: Based on the attributes of the job node, evaluate its resource requirements, including the specific amount of CPU, memory, and disk resources; Considering the execution logic and computational complexity of the job nodes, dynamically adjust resource requirements; Resource allocation strategy: Design resource allocation strategies based on priority and resource status; Implement resource allocation algorithms to dynamically allocate resources to job nodes based on strategies; Resource adjustment mechanism: Design resource adjustment mechanisms to allow the system to automatically adjust resource allocation when resources are tight or idle; Realize resource recovery and reuse functions and improve resource utilization.

[0011] Preferably, in S4, specifically including: Dependency analysis: Analyze the dependencies between job nodes based on the logic of the connection lines; Determine the execution order of job nodes to ensure no circular dependencies and deadlocks; Resource allocation considerations: Use resource allocation results as an important input to the scheduling algorithm; Consider the impact of resource allocation on job node execution time; Computational complexity evaluation: Evaluate the computational complexity of the job nodes, including processing time and data volume; Consider the impact of computational complexity on the overall execution time of the job flow; Scheduling strategy design: Design scheduling strategies based on dependencies, resource allocation, and computational complexity; Implement the job flow scheduling algorithm and determine the execution order and time of job nodes according to the strategy; Scheduling Optimization: Consider the parallelism and load balancing of job flows and optimize the scheduling strategy; Implement dynamic scheduling function, allowing the system to adjust scheduling strategy according to resource changes and job progress during execution.

[0012] Preferably, in S5, specifically including: Resource Monitoring: Real-time monitoring of the usage of various resources in the system; Design a resource monitoring interface to display resource usage and change trends; Dynamic Adjustment: Dynamically adjust resource allocation and job flow scheduling strategies based on resource monitoring results; Implement resource early warning function, automatically triggering adjustment mechanism when resource usage reaches the threshold; Logging: Record the process and results of resource monitoring and dynamic adjustment; Provides log query function, allowing users to view and analyze resource usage.

[0013] Preferably, in S6, specifically including: Module Integration: Integrate the job flow model, visual modeling interface, resource allocation algorithm and job flow scheduling algorithm module into the flow scheduling engine; Design interfaces and data interaction between modules; Engine Optimization: Optimize the performance of the stream scheduling engine to improve processing speed and stability; Design an exception handling mechanism to ensure the normal operation of the engine under abnormal circumstances; API interface design: Provides an API interface for the flow scheduling engine, allowing other systems or modules to call it; Design security and permission control mechanisms for API interfaces.

[0014] Preferably, in S7, the method specifically includes: Test case design: Design detailed test cases based on the functions and requirements of the flow scheduling engine; Consider various boundary conditions and abnormal situations to ensure the comprehensiveness of the test; Test execution: Use test cases to test the stream scheduling engine and record the test results; Analyze the test results to find out the existing problems and deficiencies; Optimization and improvement: Based on the test results, the flow scheduling engine was optimized and improved; The testing and optimization process is repeated until the expected performance and stability requirements are achieved.

[0015] Preferably, in S8, the method specifically includes: Deployment preparation: Prepare the deployment environment, including hardware resources, operating system, and database; Configure the parameters and settings of the flow scheduling engine; Deployment and implementation: Deploy the optimized stream scheduling engine to the deployment environment; Conduct post-deployment testing to ensure the normal operation of the engine in the actual environment; User Training: Provide users with training and technical support on the use of the flow scheduling engine; Provide user manuals and operation guides to help users get started quickly; Application monitoring and maintenance: Continuously monitor and maintain the flow scheduling engine to ensure its long-term stable operation; The flow scheduling engine is continuously improved and optimized based on user feedback and needs.

[0016] The above technical solution of the present invention has the following beneficial technical effects: Through intelligent scheduling algorithms, dynamic adjustments can be made based on real-time resource conditions and job flow characteristics to improve the execution efficiency and success rate of job flows; through resource allocation and monitoring, resources can be fully utilized and resource consumption and waste can be reduced; through a visual modeling interface, users can easily build and manage job flows, improving user experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention provides a flowchart of a method for constructing a visual modeling workflow scheduling engine. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0019] like Figure 1 As shown, a method for constructing a visual modeling workflow scheduling engine proposed by the present invention includes the following steps: S1. Define the workflow model First, you need to define the job flow model, including job nodes, connecting lines, and job attributes. Job nodes represent each link in the job flow, and connecting lines represent the logical relationship between job nodes. Specifically include: Job node definition: Determine the type of job node: data processing node, computing node, decision node; Define the properties of a node including: node ID, name, description, input / output parameters, and execution logic; Design node status mechanisms, including pending, executing, completed, and failed; Connection line definition: Determine the type of connection line (such as sequential connection, parallel connection, conditional connection); Define the properties of the connection line, including the connection ID, start node, end node, and conditional expression.

[0020] Design the logic of the connection lines to ensure the correct dependencies between job nodes; Job property definition: Define the overall properties of the job flow, such as name, description, and version number; Define specific properties for each job node, such as resource requirements, priority, and computational complexity.

[0021] It should be noted that in this step, the core task is to clarify the basic structure of the job flow model. The job flow model mainly consists of three parts: job nodes, connecting lines, and job attributes. Job nodes represent specific links in the job flow and are the basic units of task execution. Connecting lines reveal the logical relationship between job nodes and clarify the order and dependency of task execution. Job attributes further refine the characteristics of job nodes, including node ID, name, description, input / output parameters, and execution logic, providing an important basis for subsequent resource allocation and scheduling.

[0022] S2. Build a visual modeling interface According to the workflow model, a visual modeling interface is constructed, providing drag and drop and connection functions, so that users can easily build workflows; Specifically include: Interface design: Design user-friendly interface layout, including toolbar, node library, and canvas area; Implement the drag-and-drop function, allowing users to drag nodes from the node library to the canvas; Implemented the connection function, allowing users to create connection lines by clicking and dragging on the canvas; Property configuration: Provides a property configuration panel for the selected node or connection line, allowing users to modify its properties; Implement real-time preview function to show the impact of user-configured properties on the job flow; Save and Load: Implement the job flow saving function, allowing users to save the constructed job flow to a file or database; Implement the job flow loading function, allowing users to load existing job flows from files or databases.

[0023] It should be noted that based on the defined workflow model, a user-friendly visual modeling interface needs to be built next. This interface should have drag and drop and connection functions, allowing users to easily build and modify workflows. The interface design needs to take user experience into consideration, provide a clear toolbar, node library, and canvas area, and support real-time preview functions, so that users can intuitively see the impact of configured properties on the workflow. At the same time, the workflow saving and loading functions need to be implemented to facilitate users to manage and reuse workflows at different time points.

[0024] S3. Design resource allocation algorithm Design a resource allocation algorithm that can dynamically allocate resources to job nodes based on current resource status and job priority factors; Algorithm formula: Ri = f(Si, Pi, Ai); Among them, Ri represents the amount of resources allocated to job node i, Si represents the resource demand of job node i, Pi represents the priority of job node i, Ai represents the current amount of available resources, and f represents the resource allocation function; Specifically include: Resource information collection: Collect status information of various resources in the system in real time, such as CPU usage, memory usage, and disk space; Design resource information update mechanism to ensure the timeliness and accuracy of resource information; Resource Needs Assessment: Based on the attributes of the job node, evaluate its resource requirements, including the specific amount of CPU, memory, and disk resources; Considering the execution logic and computational complexity of the job nodes, dynamically adjust resource requirements; Resource allocation strategy: Design resource allocation strategies based on priority and resource status; Implement resource allocation algorithms to dynamically allocate resources to job nodes based on strategies; Resource adjustment mechanism: Design resource adjustment mechanisms to allow the system to automatically adjust resource allocation when resources are tight or idle; Realize resource recovery and reuse functions and improve resource utilization.

[0025] It should be noted that the algorithm needs to comprehensively consider the real-time resource situation and the characteristics of the job flow to dynamically allocate resources to the job nodes. The implementation of the algorithm requires collecting resource status information in the system, such as CPU usage and memory occupancy, and calculating the amount of resources that each job node should obtain through the resource allocation function based on the resource requirements, priority and current available resource factors of the job node. At the same time, the algorithm also needs to design a resource adjustment mechanism to cope with the automatic adjustment needs when resources are tight or idle, and improve resource utilization.

[0026] S4. Design of job flow scheduling algorithm Design a job flow scheduling algorithm based on job flow characteristics and resource allocation results. This algorithm can determine the execution order and time of job nodes according to the logical relationship between job nodes and resource allocation. Algorithm formula: Ti = g(Di,Ri,Ci); Among them, Ti represents the execution time of job node i, Di represents the dependency of job node i, Ri represents the amount of resources allocated to job node i, Ci represents the computational complexity of job node i, and g represents the job flow scheduling function; Specifically include: Dependency analysis: Analyze the dependencies between job nodes based on the logic of the connection lines; Determine the execution order of job nodes to ensure no circular dependencies and deadlocks; Resource allocation considerations: Use resource allocation results as an important input to the scheduling algorithm; Consider the impact of resource allocation on job node execution time; Computational complexity evaluation: Evaluate the computational complexity of the job nodes, including processing time and data volume; Consider the impact of computational complexity on the overall execution time of the job flow; Scheduling strategy design: Design scheduling strategies based on dependencies, resource allocation, and computational complexity; Implement the job flow scheduling algorithm and determine the execution order and time of job nodes according to the strategy; Scheduling Optimization: Consider the parallelism and load balancing of job flows and optimize the scheduling strategy; Implement dynamic scheduling function, allowing the system to adjust scheduling strategy according to resource changes and job progress during execution.

[0027] It should be noted that the dependency analysis: Traverse all job nodes in the job flow model and build a dependency graph.

[0028] Use topological sorting or depth-first search (DFS) to determine the execution order of job nodes.

[0029] Resource allocation results integration: According to the resource allocation algorithm in S3, obtain the resource allocation results of each job node.

[0030] Integrate the resource allocation results into the dependency graph and add resource attributes to each job node.

[0031] Computational complexity evaluation: Evaluate the complexity of each job node and determine its processing time requirement C i .

[0032] Complexity assessment can be based on historical data, empirical formulas, or machine learning models.

[0033] Scheduling algorithm execution: Use the job flow scheduling function g, combined with dependencies, resource allocation results and computational complexity, to calculate the execution time T of each job node i .

[0034] Based on the execution time, determine the execution order and schedule of the job nodes.

[0035] Optimization and adjustment: Optimize and adjust the job flow scheduling results according to actual conditions and needs.

[0036] Add buffers and adjust resource allocation strategies to improve the efficiency and stability of job flow execution.

[0037] Output scheduling results: The job flow scheduling results are output in visual or text form for users to review and confirm.

[0038] Users can further adjust and optimize the job flow based on the scheduling results.

[0039] It should be noted that the job flow scheduling algorithm is responsible for determining the execution order and time of the job nodes. The algorithm is based on the logical relationship between job nodes and resource allocation factors, and calculates the execution time of each job node through the job flow scheduling function. At the same time, the algorithm also needs to consider the parallelism and load balancing factors of the job flow, and optimize the scheduling strategy to improve the execution efficiency of the job flow. When resources are tight and the job progress changes, the algorithm should also have the ability to dynamically adjust to cope with uncertain factors.

[0040] S5. Implement resource monitoring and dynamic adjustment Implement resource monitoring function, obtain resource usage in real time, and dynamically adjust resource allocation and job flow scheduling strategies based on resource monitoring results; Specifically include: Resource Monitoring: Real-time monitoring of the usage of various resources in the system; Design a resource monitoring interface to display resource usage and change trends; Dynamic Adjustment: Dynamically adjust resource allocation and job flow scheduling strategies based on resource monitoring results; Implement resource early warning function, automatically triggering adjustment mechanism when resource usage reaches the threshold; Logging: Record the process and results of resource monitoring and dynamic adjustment; Provides log query function, allowing users to view and analyze resource usage.

[0041] S6. Build a flow scheduling engine Integrate the above algorithms and modules into the flow scheduling engine to form a complete job flow scheduling system; Specifically include: Module Integration: Integrate the job flow model, visual modeling interface, resource allocation algorithm and job flow scheduling algorithm module into the flow scheduling engine; Design interfaces and data interaction between modules; Engine Optimization: Optimize the performance of the stream scheduling engine to improve processing speed and stability; Design an exception handling mechanism to ensure the normal operation of the engine under abnormal circumstances; API interface design: Provides an API interface for the flow scheduling engine, allowing other systems or modules to call it; Design security and permission control mechanisms for API interfaces.

[0042] S7, Testing and Optimization Test the constructed stream scheduling engine to verify its performance and stability, and optimize it based on the test results; S8. Deployment and Application Deploy the optimized flow scheduling engine to actual application scenarios to provide users with efficient and reliable job flow scheduling services.

[0043] In S7, it specifically includes: Test case design: Design detailed test cases based on the functions and requirements of the flow scheduling engine; Consider various boundary conditions and abnormal situations to ensure the comprehensiveness of the test; Test execution: Use test cases to test the stream scheduling engine and record the test results; Analyze the test results to find out the existing problems and deficiencies; Optimization and improvement: Based on the test results, the flow scheduling engine was optimized and improved; The testing and optimization process is repeated until the expected performance and stability requirements are achieved.

[0044] Preferably, in S8, the method specifically includes: Deployment preparation: Prepare the deployment environment, including hardware resources, operating system, and database; Configure the parameters and settings of the flow scheduling engine; Deployment and implementation: Deploy the optimized stream scheduling engine to the deployment environment; Conduct post-deployment testing to ensure the normal operation of the engine in the actual environment; User Training: Provide users with training and technical support on the use of the flow scheduling engine; Provide user manuals and operation guides to help users get started quickly; Application monitoring and maintenance: Continuously monitor and maintain the flow scheduling engine to ensure its long-term stable operation; The flow scheduling engine is continuously improved and optimized based on user feedback and needs.

[0045] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A method for constructing a visual modeling workflow scheduling engine, characterized in that: The following steps are involved: S1. Define the workflow model Define the operation flow model, including operation nodes, connection lines, and operation attributes. The operation nodes represent each link in the operation flow, and the connection lines represent the logical relationship between the operation nodes. S2. Build a visual modeling interface According to the workflow model, a visual modeling interface is constructed, providing drag and drop and connection functions, so that users can easily build workflows; S3. Design resource allocation algorithm Design a resource allocation algorithm that can dynamically allocate resources to job nodes based on current resource status and job priority factors; Algorithm formula: R i =f(S i ,P i ,A i ); Among them, Ri represents the amount of resources allocated to job node i, S i represents the resource requirement of job node i, P i represents the priority of job node i, A i represents the current available resources, and f represents the resource allocation function; S4. Design of job flow scheduling algorithm Design a job flow scheduling algorithm based on job flow characteristics and resource allocation results. This algorithm can determine the execution order and time of job nodes according to the logical relationship between job nodes and resource allocation. Algorithm formula: T i =g(D i ,R i ,C i ); Among them, T i represents the execution time of job node i, D i represents the dependency relationship of job node i, R i represents the amount of resources allocated to job node i, C i represents the computational complexity of job node i, and g represents the job flow scheduling function; S5. Implement resource monitoring and dynamic adjustment Implement resource monitoring function, obtain resource usage in real time, and dynamically adjust resource allocation and job flow scheduling strategies based on resource monitoring results; S6. Build a flow scheduling engine Integrate the above algorithms and modules into the flow scheduling engine to form a complete job flow scheduling system; S7, Testing and Optimization Test the constructed stream scheduling engine to verify its performance and stability, and optimize it based on the test results; S8. Deployment and Application Deploy the optimized flow scheduling engine to actual application scenarios to provide users with efficient and reliable job flow scheduling services.

2. The method for constructing a visual modeling workflow scheduling engine according to claim 1, characterized in that: In S1, it specifically includes: Job node definition: Determine the type of job node: data processing node, computing node, decision node; Define the properties of a node including: node ID, name, description, input / output parameters, and execution logic; Design node status mechanisms, including pending, executing, completed, and failed; Connection line definition: Determine the type of connecting line; Define the properties of the connection line, including connection ID, start node, end node, and conditional expression; Design the logic of the connection lines; Job property definition: Define the overall properties of the job flow; Define specific properties for each job node.

3. The method for constructing a visual modeling workflow scheduling engine according to claim 2, characterized in that: In S2, it specifically includes: Interface design: Design user-friendly interface layout, including toolbar, node library, and canvas area; Implement the drag-and-drop function, allowing users to drag nodes from the node library to the canvas; Implemented the connection function, allowing users to create connection lines by clicking and dragging on the canvas; Property configuration: Provides a property configuration panel for the selected node or connection line, allowing users to modify its properties; Implement real-time preview function to show the impact of user-configured properties on the job flow; Save and Load: Implement the job flow saving function, allowing users to save the constructed job flow to a file or database; Implement the job flow loading function, allowing users to load existing job flows from files or databases.

4. The method for constructing a visual modeling workflow scheduling engine according to claim 3, characterized in that: In S3, it specifically includes: Resource information collection: Collect status information of various resources in the system in real time; Design resource information update mechanism; Resource Needs Assessment: Based on the attributes of the job node, evaluate its resource requirements, including the specific amount of CPU, memory, and disk resources; Considering the execution logic and computational complexity of the job nodes, dynamically adjust resource requirements; Resource allocation strategy: Design resource allocation strategies based on priority and resource status; Implement resource allocation algorithms to dynamically allocate resources to job nodes based on strategies; Resource adjustment mechanism: Design resource adjustment mechanisms to allow the system to automatically adjust resource allocation when resources are tight or idle; Realize resource recovery and reuse functions and improve resource utilization.

5. The method for constructing a visual modeling workflow scheduling engine according to claim 4, characterized in that: In S4, it specifically includes: Dependency analysis: Analyze the dependencies between job nodes based on the logic of the connection lines; Determine the execution order of job nodes to ensure no circular dependencies and deadlocks; Resource allocation considerations: Use resource allocation results as an important input to the scheduling algorithm; Consider the impact of resource allocation on job node execution time; Computational complexity evaluation: Evaluate the computational complexity of the job nodes, including processing time and data volume; Consider the impact of computational complexity on the overall execution time of the job flow; Scheduling strategy design: Design scheduling strategies based on dependencies, resource allocation, and computational complexity; Implement the job flow scheduling algorithm and determine the execution order and time of job nodes according to the strategy; Scheduling Optimization: Consider the parallelism and load balancing of job flows and optimize the scheduling strategy; Implement dynamic scheduling function, allowing the system to adjust scheduling strategy according to resource changes and job progress during execution.

6. The method for constructing a visual modeling workflow scheduling engine according to claim 5, characterized in that: In S5, it specifically includes: Resource Monitoring: Real-time monitoring of the usage of various resources in the system; Design a resource monitoring interface to display resource usage and change trends; Dynamic Adjustment: Dynamically adjust resource allocation and job flow scheduling strategies based on resource monitoring results; Implement resource early warning function, automatically triggering adjustment mechanism when resource usage reaches the threshold; Logging: Record the process and results of resource monitoring and dynamic adjustment; Provides log query function, allowing users to view and analyze resource usage.

7. The method for constructing a visual modeling workflow scheduling engine according to claim 6, characterized in that: In S6, it specifically includes: Module Integration: Integrate the job flow model, visual modeling interface, resource allocation algorithm and job flow scheduling algorithm module into the flow scheduling engine; Design interfaces and data interaction between modules; Engine Optimization: Optimize the performance of the stream scheduling engine to improve processing speed and stability; Design an exception handling mechanism to ensure the normal operation of the engine under abnormal circumstances; API interface design: Provides an API interface for the flow scheduling engine, allowing other systems or modules to call it; Design security and permission control mechanisms for API interfaces.

8. The method for constructing a visual modeling workflow scheduling engine according to claim 7, characterized in that: In S7, it specifically includes: Test case design: Design detailed test cases based on the functions and requirements of the flow scheduling engine; Consider various boundary conditions and abnormal situations to ensure the comprehensiveness of the test; Test execution: Use test cases to test the stream scheduling engine and record the test results; Analyze the test results to find out the existing problems and deficiencies; Optimization and improvement: Based on the test results, the flow scheduling engine was optimized and improved; The testing and optimization process is repeated until the expected performance and stability requirements are achieved.

9. The method for constructing a visual modeling workflow scheduling engine according to claim 8, characterized in that: In S8, it specifically includes: Deployment preparation: Prepare the deployment environment, including hardware resources, operating system, and database; Configure the parameters and settings of the flow scheduling engine; Deployment and implementation: Deploy the optimized stream scheduling engine to the deployment environment; Conduct post-deployment testing to ensure the normal operation of the engine in the actual environment; User Training: Provide users with training and technical support on the use of the flow scheduling engine; Provide user manuals and operation guides to help users get started quickly; Application monitoring and maintenance: Continuously monitor and maintain the flow scheduling engine; The flow scheduling engine is continuously improved and optimized based on user feedback and needs.

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