Multi-layer nested service visual scheduling engine and method based on graph structure

Abstract

The invention relates to a multi-layer nested service visual scheduling engine and method based on a graph structure, and belongs to the technical field of visual programming. The engine comprises a visual design module, a graph analysis module and a logic judgment module which are in communication connection with one another; the visual design module is used for constructing a business flow chart by dragging nodes, drawing edges and setting nested nodes, and the chart analysis module is used for converting the business flow chart into a task execution plan. And the logic judgment module is used for determining a concurrent and serial execution sequence of a plurality of tasks and an execution path of a conditional branch according to the task execution plan, and generating a logic judgment result.

Description

Technical Field

[0001] This invention belongs to the field of visual programming technology, specifically relating to a multi-layered nested business visualization scheduling engine and method based on graph structure. Background Technology

[0002] With the development of computer technology and the economy, business processes are becoming increasingly complex and increasingly reliant on computer platforms for distribution and processing. In this field, visual scheduling is a commonly used solution due to its intuitiveness and ease of operation.

[0003] Traditional business scheduling systems suffer from numerous problems, such as their inability to handle complex concurrent serial and compound logic. To address this, Chinese Patent Publication CN102707952A discloses a user-described programming method for embedded heterogeneous multi-core processors. The steps are as follows: the user describes the heterogeneous multi-core processor platform and tasks through a graphical interface configuration wizard, sets the parallel mode, creates and registers primitive tasks, generates a Directed Acyclic Graph (DAG), and statically allocates primitive tasks on the heterogeneous multi-core processor. This expresses the processor platform characteristics, parallel requirements, and task assignments in the form of a configuration file (XML). Then, the primitive tasks parsed from the configuration file are embedded into the task tags of the heterogeneous multi-core framework code, constructing the corresponding serial source program. After calling a serial compilation tool, executable code for the heterogeneous multi-core processor is finally generated. This effectively avoids the parallel programming practices such as developing parallel compilers, creating parallel programming languages, and porting parallel libraries on general-purpose PCs or high-performance computing platforms, significantly reducing the difficulty of developing parallel programs on embedded heterogeneous multi-core processor platforms. It achieves the goal of parallel programming based on user description and a parallelization interactive wizard.

[0004] However, in the above invention, the DAG task graph only serves static task allocation, and its effect is only to optimize the utilization of computing resources. However, its adaptability is weak when facing real-time changes in multi-layered nested business. Therefore, a graph-based multi-layered nested business visualization scheduling engine and method that takes into account dynamic allocation capabilities is needed for multi-layered nested business. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a graph-based multi-layer nested business visualization scheduling engine and method, which features dynamic allocation capability for multi-layer nested businesses.

[0006] The objective of this invention can be achieved through the following technical solutions: A multi-layered nested business visualization scheduling engine based on graph structure includes a visualization design module, a graph parsing module, and a logic judgment module that are interconnected. The visualization design module is used to construct a business process diagram by dragging and dropping nodes, drawing edges, and setting nested nodes. The graph parsing module is used to convert the business process diagram into a task execution plan. The logic judgment module is used to determine the concurrent and serial execution order of several tasks and the execution path of conditional branches according to the task execution plan, and generate a logic judgment result.

[0007] As a preferred embodiment of the present invention, it further includes a task scheduling module and a task executor module that are interconnected. The task scheduling module is interconnected with the logic judgment module, and the task scheduling module is used to assign several tasks to the corresponding task executors for execution according to the logic judgment result.

[0008] As a preferred embodiment of the present invention, the graph parsing module is also used to perform a rationality check on the business process graph. The graph parsing module checks whether the ratio of the branch length to the number of branches in the business process graph exceeds the legality threshold, and issues an alarm when the judgment result is yes.

[0009] As a preferred embodiment of the present invention, the graph parsing module is further used to determine whether the total number of business process nodes exceeds the node number threshold, and to increase the value of the legality threshold when the node number threshold is exceeded.

[0010] As a preferred technical solution of the present invention, the graph parsing module is also used to count the total number of business process nodes J and expand the value of the legality threshold by A1 times, where A1=J / J0×c, J0 is a pre-input reference value for the number of nodes, and c is a pre-input correction coefficient.

[0011] As a preferred technical solution of the present invention, the logic judgment module is also used to count the number of concurrent tasks, calculate the risk coefficient based on the number of concurrent tasks and the total number of business process nodes, and determine whether the risk coefficient exceeds the threshold. When the judgment result is yes, the logic judgment module changes the shortest tasks in the concurrent tasks to be executed serially.

[0012] As a preferred embodiment of the present invention, it further includes a log recording module, which is used for statistics.

[0013] A graph-based multi-level nested business visualization scheduling method, applicable to the aforementioned graph-based multi-level nested business visualization scheduling engine, further includes the following steps: Step 1: Users can use the visual design module to build business process diagrams by dragging and dropping, and complete the configuration of node attributes, edge conditions, and cross-system interaction nodes; Step 2: The diagram parsing module parses the business process diagram and generates a task execution plan; Step 3: The logic judgment module analyzes the task execution plan to determine the concurrent and sequential relationships and conditional branching logic of the tasks.

[0014] The beneficial effects of this invention are as follows: (1) By setting up a logic judgment module, the concurrent and serial execution order of several tasks and the execution path of conditional branches are determined according to the task execution plan, and the logic judgment result is generated, which improves the adaptability when facing real-time changes in multi-layer nested business. (2) By having the graph parsing module check whether the ratio of branch length to branch number in the business process graph exceeds the legality threshold and issue an alarm when the judgment result is yes, the rationality check of the business process graph is completed, which improves the accuracy of dynamic allocation when facing the problem that may be faced in the complex scenario of multi-layer nested business. (3) By calculating the risk coefficient based on the number of concurrent users and the total number of business process nodes, it is determined whether the risk coefficient exceeds the threshold. When the determination result is yes, efficiency is sacrificed to enable a more conservative allocation logic, which improves the security of system operation and avoids system overload. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a system architecture diagram of the scheduling engine of the present invention; Figure 2 This invention includes a business process diagram with a multi-layered nested structure. Detailed Implementation

[0017] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0018] Please see Figure 1-2 A multi-layered nested business visualization scheduling engine based on graph structure includes a visualization design module, a graph parsing module, and a logic judgment module that are interconnected. The visualization design module is used to construct a business process diagram by dragging and dropping nodes, drawing edges, and setting nested nodes. The graph parsing module is used to convert the business process diagram into a task execution plan. The logic judgment module is used to determine the concurrent and serial execution order and the execution path of conditional branches of several tasks according to the task execution plan, and generate a logic judgment result. For multi-layered nested business processes, for example, the core business process includes order receipt, invoking the CRM system for order processing, invoking the WMS system for inventory verification, invoking the payment system for payment processing, invoking the logistics system for logistics allocation and order completion; in this process, for the "order processing sub-process" in the "core business process", a nested node is set in "order processing". The nested node of order processing includes order information verification and price calculation. Price calculation also includes a nested node. At the same time, for the "price calculation sub-process" in the "core business process", a nested node is also set in "price calculation". The nested node of price calculation includes calculating the base price, calculating the promotional price, or calculating taxes and fees; At this point, a multi-layered nested business process containing a second-level process and a third-level process is formally formed; By setting up a logic judgment module to determine the concurrent and serial execution order of several tasks and the execution path of conditional branches based on the task execution plan, and generating logic judgment results, the adaptability is improved when facing real-time changes in multi-layered nested business.

[0019] It also includes a task scheduling module and a task executor module that are interconnected. The task scheduling module is connected to the logic judgment module and is used to assign several tasks to the corresponding task executors for execution based on the logic judgment results.

[0020] The graph analysis module is also used to check the rationality of the business process diagram. Specifically, the graph analysis module checks whether the ratio of the branch length of the longest branch in the business process diagram to the number of branches exceeds the legality threshold, and issues an alarm when the judgment result is yes. When the ratio of the length of the longest branch to the number of branches in a process is too large, it means that the length of the longest branch is too long. There is a possibility that the branches are not allocated reasonably and all nodes are stacked into the same branch process. In this case, it is necessary to remind the user. The graph analysis module is also used to determine whether the total number of business process nodes exceeds the node number threshold, and to increase the value of the validity threshold when it exceeds the node number threshold.

[0021] The graph analysis module is also used to count the total number of business process nodes J and to expand the value of the validity threshold by A1 times, where A1 = J / J0 × c, J0 is the pre-input reference value of the number of nodes, and c is the pre-input correction coefficient; When there are many summary points and the business process is long, there is a high probability that the overall process will be long. Even if the business process is designed normally, the validity threshold will still be triggered. Therefore, it is necessary to increase the value of the validity threshold. When J is large, the value of A1=J / J0×c is large. When the value of the validity threshold is increased by A1 times, the validity threshold is increased. By enabling the graph analysis module to check whether the ratio of branch length to number of branches in the business process graph exceeds the legality threshold and to issue an alert when the result is yes, the module completes the rationality check of the business process graph, thereby improving the accuracy of dynamic allocation when facing complex scenarios such as multi-layered nested business processes.

[0022] It also includes a logging module, which is electrically connected to the task executor and is used to collect task execution information.

[0023] For the logging module, at least a log database is included. The data in the log database includes process ID, timestamp, and node name, such as 2025-05-21 10:30:45, order received successfully.

[0024] Business processes often include cross-system interactions. Therefore, a unified interface adaptation layer is also included. The unified interface adaptation layer communicates with the task scheduling module. When the task scheduling module receives the logical judgment result from the logical judgment module, and when the task involves cross-system interactions, it coordinates the unified interface adaptation layer to perform data interaction. Specifically, the operation process of the unified interface adaptation layer is transformed into a unified RESTful format for ERP systems, a unified RPC format for WMS systems, and a unified SOAP format for CRM systems.

[0025] When the logic judgment module automatically allocates task chains, if the number of parallel tasks is too large and the total business process is too long, there is a probability that the system will be overloaded. At this time, a more conservative allocation strategy is needed to adjust the allocation strategy. To this end, the logic judgment module is also used to count the number of concurrent tasks and calculate the risk coefficient based on the number of concurrent tasks and the total number of business process nodes. It judges whether the risk coefficient exceeds the threshold. When the judgment result is yes, it means that the number of parallel tasks is too large and the total business process is too long, which is likely to cause the system to be overloaded. At this time, the logic judgment module will change the shortest tasks among the concurrent tasks to be executed serially. In the process of calculating the risk coefficient based on the number of concurrent tasks and the total number of business process nodes, the number of parallel tasks R and the total number of business process nodes J are statistically analyzed, and the value of R×J×d is calculated as the risk coefficient, where d is a pre-input constant. Then, it is determined whether the risk coefficient exceeds the threshold. Specifically, suppose the tasks to be executed in parallel are a, b, c, d and e, where b has the shortest process. At the same time, there are tasks f, g and h to be executed serially after a. When the risk coefficient exceeds the threshold, the allocation strategy is adjusted and the tasks to be executed in parallel are changed to a, c, d and e, while the tasks to be executed serially after a are b, f, g and h. By calculating the risk coefficient based on the number of concurrent users and the total number of business process nodes, it is determined whether the risk coefficient exceeds the threshold. When the result is yes, a more conservative allocation logic is enabled by sacrificing some efficiency, which improves the security of system operation and avoids system overload.

[0026] A graph-based multi-level nested business visualization scheduling method, applicable to the aforementioned graph-based multi-level nested business visualization scheduling engine, includes the following steps: Step 1: Users can use the visual design module to build business process diagrams by dragging and dropping, and complete the configuration of node attributes, edge conditions, and cross-system interaction nodes; Step 2: The diagram parsing module parses the business process diagram and generates a task execution plan; Step 3: The logic judgment module analyzes the task execution plan to determine the concurrent and sequential relationships and conditional branching logic of the tasks.

[0027] The overall process of this invention: The visualization design module is used to design visual business process diagrams and write them to the computer. The diagram parsing module reads the business process diagram and then sends it to the logic judgment module. After the logic judgment module completes its judgment, it sends it to the task scheduling module. The task scheduling module instructs the task executor to execute the plan. The task scheduling module performs cross-system adaptation through a unified interface adaptation layer, calls different external business systems, generates results, and returns them to the task executor through data mapping and transformation. Finally, the task executor outputs logs to the log recording module.

[0028] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-layered nested business visualization scheduling engine based on graph structure, characterized in that: It includes a visual design module, a graph analysis module, and a logic judgment module that are interconnected. The visual design module is used to construct a business process diagram by dragging and dropping nodes, drawing edges, and setting nested nodes. The graph analysis module is used to convert the business process diagram into a task execution plan. The logic judgment module is used to determine the concurrent and serial execution order of several tasks and the execution path of conditional branches according to the task execution plan, and generate a logic judgment result.

2. The multi-layer nested business visualization scheduling engine based on graph structure according to claim 1, characterized in that: It also includes a task scheduling module and a task executor module that are interconnected. The task scheduling module is interconnected with the logic judgment module. The task scheduling module is used to assign several tasks to the corresponding task executors for execution based on the logic judgment results.

3. The multi-layer nested business visualization scheduling engine based on graph structure according to claim 1, characterized in that: The graph parsing module is also used to perform a reasonableness check on the business process graph. The graph parsing module checks whether the ratio of the branch length to the number of branches in the business process graph exceeds the legality threshold, and issues an alarm when the judgment result is yes.

4. The multi-layer nested business visualization scheduling engine based on graph structure according to claim 3, characterized in that: The graph parsing module is also used to determine whether the total number of business process nodes exceeds the node number threshold, and to increase the value of the validity threshold when it exceeds the node number threshold.

5. The multi-layer nested business visualization scheduling engine based on graph structure according to claim 4, characterized in that: The graph parsing module is also used to count the total number of business process nodes J and to expand the value of the legality threshold by a factor of A1, where A1 = J / J0 × c, J0 is a pre-input reference value for the number of nodes, and c is a pre-input correction coefficient.

6. The multi-layer nested business visualization scheduling engine based on graph structure according to claim 5, characterized in that: The logic judgment module is also used to count the number of concurrent tasks and calculate the risk coefficient based on the number of concurrent tasks and the total number of business process nodes. It determines whether the risk coefficient exceeds the threshold. If the judgment result is yes, the logic judgment module changes the shortest tasks in the concurrent tasks to be executed serially.

7. The multi-layer nested business visualization scheduling engine based on graph structure according to claim 2, characterized in that: It also includes a log recording module, which is communicatively connected to the task scheduling module and the task executor, and is used to collect task execution information.

8. A multi-layered nested business visualization scheduling method based on graph structure, characterized in that: The graph-based multi-layer nested business visualization scheduling engine according to any one of claims 1 to 6 further includes the following steps: Step 1: Users can use the visual design module to build business process diagrams by dragging and dropping, and complete the configuration of node attributes, edge conditions, and cross-system interaction nodes; Step 2: The diagram parsing module parses the business process diagram and generates a task execution plan; Step 3: The logic judgment module analyzes the task execution plan to determine the concurrent and sequential relationships and conditional branching logic of the tasks.

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