Method, system and electronic device for execution of business process model

Abstract

This application relates to the field of low-code platform technology and provides a method, system, and electronic device for executing a business process model. This application evaluates the process priority corresponding to each process node using historical execution data associated with those nodes, establishes process rules based on the process priorities, and optimizes the process rules in real time during the execution of the business process model according to the process rules, combining current execution data and process priorities. It establishes and adjusts process rules by constructing a two-layer optimization model that integrates historical and current execution data. The first layer evaluates process priorities based on historical execution data and pre-optimizes the process rules; the second layer dynamically adjusts the process rules during execution, taking into account real-time changes represented by current execution data. This makes the formulation and adjustment of process rules more comprehensive and timely, thereby significantly improving the execution efficiency, throughput, and resource utilization of the business process model.

Description

Technical Field

[0001] This application relates to the field of low-code platform technology, specifically to a method, system, and electronic device for executing a business process model. Background Technology

[0002] With the deepening of information technology and digital transformation, business processes in enterprises and organizations are becoming increasingly complex. To quickly respond to business changes and reduce development and maintenance costs, low-code platforms have emerged and gained widespread application. In low-code platforms, business users can quickly build and deploy business process models by visually dragging and dropping components and configuring parameters. Some existing process management systems or low-code platforms already possess preliminary optimization capabilities. For example, they can identify potential performance bottlenecks by analyzing historical execution data of process nodes, or determine the next branch path based on real-time data entered at a certain node in the process. These technologies, to a certain extent, improve the flexibility of process construction and the level of automation in execution.

[0003] However, existing technologies, when optimizing process execution using data, typically rely on only a single dimension or isolated execution data. For example, some solutions perform static optimization based solely on historical data, while others simply route based on a single field of the current task, failing to effectively combine historical data with the current execution status. This results in a lack of holistic and forward-looking considerations in process rule adjustments and decisions, leading to rather one-sided judgments. Especially in complex business scenarios, the scheduling strategies for automated execution are far from optimal, and the overall execution efficiency and resource utilization of the process remain very low. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] In view of the shortcomings of the prior art described above, this application provides a method, system and electronic device for executing a business process model, so as to improve the execution efficiency of the business process model.

[0006] In a first aspect, this application provides a method for executing a business process model, comprising: acquiring a business process model, wherein the business process model includes multiple process nodes; evaluating the process priority corresponding to each process node based on historical execution data associated with each process node, so as to establish process rules between each process node based on the process priority, wherein the historical execution data is obtained by data collection when the process node is in an execution state; executing each process node in the business process model according to the process rules; and in response to any process node being in an execution state, acquiring the current execution data of each process node through data collection, so as to evaluate the execution priority corresponding to each process node based on the process priority and the current execution data, and adjusting the process rules in real time based on the execution priority.

[0007] In one embodiment of this application, obtaining a business process model includes: obtaining process node information input by the user through a preset low-code platform, and constructing process nodes based on the process node information, wherein the process node information includes at least one of node definition, task type, execution role, data flow method, and external interface.

[0008] In one embodiment of this application, the process priority corresponding to each process node is evaluated based on the historical execution data associated with each process node, including at least one of the following: if the historical execution data associated with a process node includes node execution duration, then the process priority corresponding to the process node is determined according to the node execution duration based on a negative correlation; if the historical execution data associated with a process node includes the growth frequency corresponding to the node execution duration, then the process priority corresponding to the process node is determined according to the growth frequency corresponding to the node execution duration based on a negative correlation; if the historical execution data associated with a process node includes execution success rate, then the process priority corresponding to the process node is determined according to the execution success rate based on a positive correlation; if... If the historical execution data associated with a process node includes the frequency of anomalies, then the process priority corresponding to the process node is determined according to the negative correlation based on the frequency of anomalies; if the historical execution data associated with a process node includes the growth frequency corresponding to the frequency of anomalies, then the process priority corresponding to the process node is determined according to the negative correlation based on the growth frequency corresponding to the frequency of anomalies; if the historical execution data associated with a process node includes resource utilization, then the process priority corresponding to the process node is determined according to the negative correlation based on the resource utilization; if the historical execution data associated with a process node includes task waiting time, then the process priority corresponding to the process node is determined according to the positive correlation based on the task waiting time.

[0009] In one embodiment of this application, establishing process rules between each process node according to the process priority includes: matching initial rules between each process node from a preset rule base according to each process node, wherein the initial rules include at least one of logical relationship, triggering condition, execution order, and branch path; adjusting the initial rules according to the process priority to obtain process rules between each process node.

[0010] In one embodiment of this application, executing each process node in the business process model according to the process rules includes: determining any two process nodes as a first node and a second node respectively; determining whether there is a dependency relationship between the first node and the second node according to the process rules, wherein the dependency relationship includes at least one of node dependency relationship, data dependency relationship and resource dependency relationship; if there is no dependency relationship between the first node and the second node, then executing the first node and the second node in parallel; if there is a dependency relationship between the first node and the second node, then executing the first node and the second node sequentially according to the dependency relationship.

[0011] In one embodiment of this application, the execution priority corresponding to each process node is evaluated based on the process priority and the current execution data, including at least one of the following: determining the execution priority corresponding to the process node according to a positive correlation based on the process priority corresponding to the process node; if the current execution data corresponding to the process node includes task urgency, then determining the execution priority corresponding to the process node according to the task urgency according to a positive correlation; if the current execution data corresponding to the process node includes user permission level, then determining the execution priority corresponding to the process node according to the user permission level according to a positive correlation; if the current execution data corresponding to the process node includes task waiting time, then determining the execution priority corresponding to the process node according to the task waiting time according to a positive correlation.

[0012] In one embodiment of this application, evaluating the execution priority corresponding to each process node based on the process priority and the current execution data includes: pre-setting priority weights corresponding to each target priority, wherein the target priority includes the process priority and the current priority, and the current priority is obtained by matching the current execution data; obtaining data statistics results by performing data statistics on each of the historical execution data, so as to evaluate the historical meta-state of the business process model based on the data statistics results; and monitoring the task urgency of each of the current execution data, so as to evaluate the current meta-state of the business process model based on the monitoring results, wherein the data statistics results include historical data confidence and / or historical data. The confidence level of the historical data is calculated based on the timeliness distribution and dispersion. If the historical meta-state and the current meta-state satisfy a first preset condition, the priority weight corresponding to the process priority is increased, wherein the first preset condition includes the historical meta-state being a stable state and the current meta-state being a regular execution state. If the historical meta-state and the current meta-state satisfy a second preset condition, the priority weight corresponding to the current priority is increased, wherein the second preset condition includes the historical meta-state being an exploratory state and the current meta-state being an emergency execution state. The execution priority corresponding to any process node is obtained by weighting the target priority according to each priority weight.

[0013] In one embodiment of this application, after executing each process node in the business process model according to the process rules, the method further includes: detecting logical verification information of the business process model during execution, wherein the logical verification information includes at least one of logical relationship loop, trigger condition conflict, and branch path not closed; and generating correction suggestions corresponding to the business process model based on the detected logical verification information.

[0014] Secondly, this application also provides an execution system for a business process model, comprising: a low-code platform for acquiring a business process model, wherein the business process model includes multiple process nodes; an adaptive rule engine for evaluating the process priority corresponding to each process node based on historical execution data associated with each process node, so as to establish process rules between each process node based on the process priority, wherein the historical execution data is obtained by data collection when the process node is in an execution state; a task execution module for executing each process node in the business process model according to the process rules; and a task scheduling module for acquiring the current execution data of each process node through data collection in response to any process node being in an execution state, so as to evaluate the execution priority corresponding to each process node based on the process priority and the current execution data, and adjust the process rules in real time based on the execution priority.

[0015] Thirdly, this application also provides an electronic device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method described above.

[0016] The beneficial effects of this application are: By leveraging historical execution data associated with process nodes, the priority of each node is evaluated. Based on this priority, process rules are established, achieving the first layer of optimization. Then, during the execution of the business process model according to these rules, current execution data is collected, and combined with the process priorities from the first layer of optimization, the process rules are optimized in real-time, achieving the second layer of optimization. This two-layer optimization model, built using historical and current execution data, optimizes process rules before execution. The first layer pre-evaluates process priorities based on historical data, optimizing the rules before execution. The second layer incorporates real-time changes represented by current execution data, optimizing the rules during execution. This makes the formulation and optimization of business process rules more comprehensive and timely, avoiding decision-making delays or errors caused by relying on partial data in existing technologies. It ensures that process scheduling always selects the optimal path, achieving higher processing throughput and resource utilization, and improving the execution efficiency of the business process model. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0018] In the attached diagram: Figure 1 This is a schematic diagram of the system architecture for implementing a business process model execution method in an embodiment of this application; Figure 2 This is a flowchart illustrating the execution method of a business process model in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an execution system for a business process model in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer system of an electronic device in an embodiment of this application. Detailed Implementation

[0019] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0022] The terms "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0023] Unless otherwise stated, the term "multiple" means two or more.

[0024] In this application, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0025] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0026] Combination Figure 1 As shown, this application provides a system architecture for implementing an execution method of a business process model, including a low-code platform, an adaptive rule engine, a task execution module, a task scheduling module, a logic verification engine, and a visualization module.

[0027] Low-code platforms are used to obtain business process models, which include multiple process nodes.

[0028] The adaptive rule engine is used to evaluate the process priority of each process node based on the historical execution data associated with each process node, so as to establish process rules between process nodes according to the process priority; the historical execution data is obtained by collecting data when the process node is in the execution state.

[0029] The task execution module is used to execute each process node in the business process model according to the process rules.

[0030] The task scheduling module is used to acquire the current execution data of each process node through data collection, so as to evaluate the execution priority of each process node according to the process priority and the current execution data, and adjust the process rules in real time according to the execution priority.

[0031] The logic verification engine is used to detect logic verification information during the execution of the business process model. The logic verification information includes at least one of the following: logical relationship loops, trigger condition conflicts, and unclosed branch paths. Based on the detected logic verification information, the engine generates correction suggestions for the corresponding business process model.

[0032] The visualization module is used to display the current execution data of each process node through a visual dashboard.

[0033] Combination Figure 2 As shown, this application provides a method for executing a business process model, including: Step S201: Obtain the business process model; The business process model includes multiple process nodes; Step S202: Based on the historical execution data associated with each process node, evaluate the process priority corresponding to each process node, so as to establish process rules between each process node according to the process priority. Historical execution data is obtained by collecting data when a process node is in the execution state. Step S203: Execute each process node in the business process model according to the process rules; Step S204: In response to any process node being in the execution state, the current execution data of each process node is acquired through data collection, so as to evaluate the execution priority of each process node according to the process priority and the current execution data, and adjust the process rules in real time according to the execution priority.

[0034] The execution method of the business process model provided in this application evaluates the process priority corresponding to each process node by using historical execution data associated with the process nodes. Based on the process priority, process rules are established, achieving the first layer of optimization. Then, during the execution of the business process model according to these rules, current execution data is collected, and combined with the process priority from the first layer of optimization, the process rules are optimized in real-time for the second layer. In this way, by establishing a two-layer optimization model for process rules using historical and current execution data, the first layer pre-evaluates process priorities based on historical execution data, optimizing the process rules before execution. The second layer combines real-time changes represented by current execution data to optimize the process rules during execution. This makes the formulation and optimization of business process rules more comprehensive and timely, avoiding decision-making delays or errors caused by relying on partial data in existing technologies. It ensures that process scheduling always selects the optimal path, achieving higher processing throughput and resource utilization, and improving the execution efficiency of the business process model.

[0035] Optionally, obtaining the business process model includes: obtaining process node information input by the user through a preset low-code platform, and constructing process nodes based on the process node information, wherein the process node information includes at least one of node definition, task type, execution role, data flow method, and external interface.

[0036] In some embodiments, the low-code platform is equipped with a visual process designer, through which users define process node information, including at least one of node definition, task type, execution role, data flow method, and external interface. The low-code platform supports node extension based on a plug-in mechanism, allowing users to customize process components and integrate external systems in a pluggable manner.

[0037] In some embodiments, users do not need to manually write a lot of code. They can use the visual process design tools of the low-code platform to define process node information by dragging and dropping components, configuring parameters, and setting node properties to create a business process model. The business process model is stored as a standardized process definition file for cross-platform parsing, version upgrades and rollbacks, and subsequent process execution.

[0038] In some embodiments, the low-code platform is implemented via the Web, providing a visual process designer. The front end of this designer can be built using modern JavaScript frameworks such as React or Vue, while the back end is responsible for model storage and parsing. When a user needs to build a new business process model, they can drag and drop preset process nodes (e.g., "approval node," "data entry node," "service call node") from the component toolbox onto the main canvas. With each drag and drop operation, the front-end application instantiates a JavaScript object representing that node and assigns it a globally unique ID. Users can define the execution order and branch paths by drawing connecting lines between nodes, and these connections are also recorded in the front-end data structure. When a user selects any node on the canvas, the property configuration panel on the right dynamically renders the configuration items for that node, where the user can enter detailed process node information.

[0039] In some embodiments, the low-code platform also provides a node extension scheme based on a plug-in mechanism. Users follow the node plug-in interface specifications defined by the platform to develop new custom process components, such as "SAP document creation node" or "electronic signature service node". After development, the plug-in package is uploaded to the platform, which automatically registers it and dynamically loads it into the component toolbox of the visual process designer, thereby achieving seamless integration with external systems. When the user completes the process design and clicks "save", the front-end application serializes the nodes, connections and all configuration attributes on the canvas into a standardized process definition file. This file preferably uses standard XML (Extensible Markup Language) format or a structured JSON (JavaScript Object Notation) format, which not only accurately describes the topology of the process and the behavior of each node, but also includes metadata such as version number and creator. This file is submitted to the back-end server, which stores it in the database for persistence, thereby realizing version control of the business process model and supporting subsequent version upgrades and rollbacks.

[0040] Optionally, based on the historical execution data associated with each process node, the process priority corresponding to each process node is evaluated, including at least one of the following: if the historical execution data associated with a process node includes the node's execution duration, then the process priority corresponding to the process node is determined according to the node's execution duration based on a negative correlation; if the historical execution data associated with a process node includes the growth frequency corresponding to the node's execution duration, then the process priority corresponding to the process node is determined according to the growth frequency corresponding to the node's execution duration based on a negative correlation; if the historical execution data associated with a process node includes the execution success rate, then the process priority corresponding to the process node is determined according to the execution success rate based on a positive correlation; if the process... If the historical execution data associated with a node includes the frequency of anomalies, then the process priority of the process node is determined according to the negative correlation based on the frequency of anomalies; if the historical execution data associated with a process node includes the growth frequency corresponding to the frequency of anomalies, then the process priority of the process node is determined according to the negative correlation based on the growth frequency corresponding to the frequency of anomalies; if the historical execution data associated with a process node includes resource utilization, then the process priority of the process node is determined according to the negative correlation based on the resource utilization; if the historical execution data associated with a process node includes task waiting time, then the process priority of the process node is determined according to the positive correlation based on the task waiting time.

[0041] In some embodiments, historical execution data includes one or more of the following: node execution time, the growth frequency of node execution time, execution success rate, anomaly occurrence frequency, the growth frequency of anomaly occurrence frequency, resource utilization rate, and task waiting time.

[0042] In some embodiments, there is a negative correlation between node execution time and process priority, wherein the longer the node execution time, the lower the process priority.

[0043] In some embodiments, there is a negative correlation between the growth frequency of node execution time and process priority. The higher the growth frequency of node execution time, the more likely the node execution time is to be continuously increasing or the frequency of abnormal increases, and the lower the process priority.

[0044] In some embodiments, there is a positive correlation between execution success rate and process priority. A higher execution success rate indicates that the process node has a good execution result and the process priority is higher.

[0045] In some embodiments, there is a negative correlation between the frequency of anomalies and the process priority, wherein the higher the frequency of anomalies, the lower the process priority.

[0046] In some embodiments, there is a negative correlation between the growth frequency of anomaly occurrence and process priority. The higher the growth frequency of anomaly occurrence, the more likely the anomaly occurrence is on an upward trend and the lower the process priority is.

[0047] In some embodiments, there is a negative correlation between resource utilization and process priority, wherein the higher the resource utilization, the lower the process priority.

[0048] In some embodiments, there is a positive correlation between task waiting time and process priority. The longer the task waiting time, the more the task waiting needs to be taken into account, and the higher the process priority is.

[0049] In some embodiments, the adaptive rule engine periodically pulls historical execution data from the database, where the historical execution data is stored in different tables and retrieved via API (Application Programming Interface) or direct database queries. To evaluate the process priority of a specific process node, the adaptive rule engine performs the following steps: extracting evaluation metrics for process priority from the historical execution data, for example, calculating the node execution duration based on task start and task end events, calculating the execution success rate by counting the total number of process node instances and the number of successfully completed instances, and obtaining the CPU (Central Processing Time) of the process node during execution through monitoring logs. Resource utilization rates, such as CPU (Central Processing Unit) utilization and memory utilization, are assessed. Due to the significant differences in the dimensions and numerical ranges of different evaluation metrics, each metric needs to be normalized, for example, using a min-max normalization method. Simultaneously, time series analysis methods are employed to determine the growth frequency of node execution time and the growth frequency of anomalies. For instance, the average daily execution time over the past 7 days is used as a data point sequence, and linear regression or slope calculation is used to determine its trend. A positive slope indicates an increasing trend; the larger the slope, the higher the growth frequency, which is mapped to a higher negative impact value. After all metrics are calculated and normalized, the adaptive rule engine calculates the final process priority using a weighted summation model. Each metric is assigned a signed weight based on its relationship with the priority (positive or negative correlation). The final calculated process priority is a comprehensive value stored in a configuration library associated with the process nodes for use in subsequent process rule establishment and adjustment steps.

[0050] Optionally, process rules between process nodes are established based on process priority, including: matching initial rules between process nodes from a preset rule base according to each process node, wherein the initial rules include at least one of logical relationship, triggering condition, execution order, and branch path; adjusting the initial rules according to process priority to obtain process rules between process nodes.

[0051] In some embodiments, the adaptive rule engine sets the logical relationships and triggering conditions between process nodes to form initial rules, and optimizes the initial rules based on process priority to obtain process rules between each process node.

[0052] In some embodiments, by parsing user-defined rules, complex logical relationships (e.g., AND, OR, NOT, etc.) are extracted to adjust the process rules between process nodes according to the business needs represented by the complex logical relationships.

[0053] In some embodiments, the adaptive rule engine traverses all process nodes in the business process model. For each potentially related pair of nodes, it queries a pre-defined rule base to match an initial rule. This rule base is a database table or configuration file storing various standard business and logical patterns. For example, if two nodes A and B are connected in the model by a directed line from A to B, the rule engine generates an initial rule defining the execution order as "execute B after A completes" and the logical relationship as "sequential dependency". Another example is if node A is connected to multiple branch nodes B, C, and D. The rule engine generates a default trigger condition for each branch path. If the branch path is an exclusive gateway, the default rule might be path_B: true, path_C: false, path_D: true. False means the first path is followed by default. After generating the initial rules, the adaptive rule engine will use the process priority of each node calculated in the previous step to adjust these initial rules, thereby generating the final process rules. For example, suppose nodes B and C are designed to be executed in parallel in the model and there is no data dependency between them. The initial rule is to start B and C at the same time. However, since their process priorities are Priority_B = 0.8 and Priority_C = 0.5 respectively, node B has a higher priority. The process rule is designed to "when the parallel gateway is activated, allocate execution resources to node B first, and start node C after node B starts", so that high-priority, dependency-free tasks can start execution faster.

[0054] In some embodiments, different process rules are used in the same business process model, and the execution results of each business process model are compared to determine whether each process rule meets the process requirements based on the comparison results. The process requirements include execution efficiency requirements and success rate requirements.

[0055] In some embodiments, the adaptive rule engine clones a business process model. One copy (Group A) is executed using the initial rules before adjustment, while the other copy (Group B) is executed using the adjusted process rules based on process priority. After running for a period of time, the system automatically compares the execution results of the two business process models. The comparison metrics include average total execution time, end-to-end execution efficiency, and overall success rate. If the comparison results show that the rules of Group B are significantly better than those of Group A, the system will determine that the adjusted process rules meet the process requirements and formally apply them as the standard execution rules for the business process model. Conversely, if the effect is not good, it will roll back or trigger a new round of rule adjustments.

[0056] Optionally, executing each process node in the business process model according to process rules includes: determining any two process nodes as the first node and the second node respectively; determining whether there is a dependency relationship between the first node and the second node according to process rules, wherein the dependency relationship includes at least one of node dependency relationship, data dependency relationship and resource dependency relationship; if there is no dependency relationship between the first node and the second node, then executing the first node and the second node in parallel; if there is a dependency relationship between the first node and the second node, then executing the first node and the second node sequentially according to the dependency relationship.

[0057] In some embodiments, the process definition file described in a standardized manner is automatically parsed to obtain the process node information corresponding to the process node. Business tasks, external interfaces, and message notifications are executed according to the trigger conditions. The process definition file can also be used to implement platform deployment, version upgrade, and version rollback. The execution of process nodes supports an asynchronous processing mechanism based on message queues to ensure the stability of the system in high-concurrency scenarios.

[0058] In some embodiments, process nodes that can be parallelized are determined from each process node according to process rules, process priority, execution priority, etc., and topology sorting and dependency graph analysis are used to achieve parallel execution of node tasks, so as to reduce the overall process time.

[0059] In some embodiments, the task execution module performs a pre-analysis before executing the business process model. It traverses all process nodes and connections in the process definition file to construct a directed acyclic graph (DAG) as a dependency graph. This dependency graph can represent various dependencies. For example, if there is a direct connection from the first node to the second node in the process graph, a node dependency is determined between them. Similarly, if an input variable of the second node originates from an output variable of the first node, a data dependency is still determined even without a direct node connection. Furthermore, for nodes configured to access exclusive external resources, resource dependencies between process nodes are determined based on their resource identifiers. Based on the constructed dependency graph, topological sorting and dependency graph analysis algorithms are used to identify all parallelizable process nodes. All nodes with an in-degree of zero are considered as the currently executable set of nodes.

[0060] In some embodiments, when there is no dependency between multiple process nodes, the multiple process nodes are executed in parallel; if there is a resource dependency between multiple process nodes, the process nodes related to the resource are executed sequentially to avoid resource contention.

[0061] In some embodiments, the identified set of executable nodes is placed into a task scheduling queue. If there are multiple process nodes in the task scheduling queue that do not have any dependencies, these nodes are executed in parallel by starting multiple worker threads or distributing tasks to the task executor cluster. If resource dependencies are identified between multiple nodes, the engine will execute these process nodes related to specific resources sequentially, even if they are parallel in the flowchart, to avoid resource contention and data inconsistency. For example, two parallel "deduct inventory" nodes, although they have no node or data dependencies, are executed sequentially because they operate on the same shared resource. This is achieved by using resource locks or placing them in the same single-threaded execution queue. If there are tasks with node or data dependencies, they are executed strictly according to the dependencies. Only after the preceding node has successfully completed execution and returned a result will the subsequent dependent node be activated and placed into the scheduling queue.

[0062] In some embodiments, the execution of the business process model is based on the process definition file. When a version upgrade is required, newly launched process instances will automatically use the latest version of the definition file, while running instances can choose to continue using the old version or migrate to the new version. Similarly, version rollback simply requires pointing the platform's default version pointer back to the old definition file version. In this way, this standardized file-based management approach, combined with intelligent scheduling strategies, ensures the efficiency, stability, and maintainability of process execution.

[0063] Optionally, the execution priority of each process node is evaluated based on the process priority and current execution data, including at least one of the following: determining the execution priority of the process node according to the positive correlation based on the process priority of the process node; if the current execution data of the process node includes the task urgency, then determining the execution priority of the process node according to the task urgency and a positive correlation based on the positive correlation based on the task urgency; if the current execution data of the process node includes the user permission level, then determining the execution priority of the process node according to the user permission level and a positive correlation based on the positive correlation based on the user permission level; if the current execution data of the process node includes the task waiting time, then determining the execution priority of the process node according to the task waiting time and a positive correlation based on the task waiting time.

[0064] In some embodiments, the execution priority of each process node is evaluated based on the process priority and the current execution data, so as to adjust the execution order and execution branch weight according to the execution priority and achieve the optimal flow path.

[0065] In some embodiments, the current execution data includes at least one of the following: task urgency, user permission level, and task waiting time.

[0066] In some embodiments, there is a positive correlation between task urgency and execution priority. The higher the task urgency, the higher the execution priority, thereby suspending the execution of process nodes with low task urgency and prioritizing the execution of process nodes with high task urgency.

[0067] In some embodiments, when a process node is executing, for any process node in a pending or unexecuted state, the process engine's scheduler calculates its execution priority in real time. This execution priority integrates static historical execution data and dynamic current execution data, including: reading the process priority calculated in the preceding steps, which represents the inherent importance of the node based on its historical performance and, according to a positive correlation, directly serves as the fundamental component for calculating the execution priority; during process initiation or execution, users or external systems can pass a task urgency parameter via API, or users can input a form... Selecting the "Expedited" option for task urgency maps tasks to a priority factor based on their urgency level, following a positive correlation. Simultaneously, when a task is assigned to a specific executor, the user's permission level is queried, and based on the positive correlation, tasks assigned to users with higher permission levels have higher execution priority. Furthermore, the waiting time of a task from its creation to the current moment is monitored, showing a positive correlation with execution priority. For example, if a task waits for more than one hour, its priority factor automatically increases by a fixed value to prevent task cancellation. Finally, the execution priority of each process node is calculated by weighting these priority factors.

[0068] In some embodiments, adjusting process rules in real time based on execution priority includes: when there are multiple nodes that can be executed in parallel in the task scheduling queue, sorting them from high to low according to the calculated execution priority, and the node with the highest execution priority will be allocated execution resources first; when encountering a gateway that needs to be manually selected, if there are significant differences in the real-time execution priority of the next node of different branch paths, the user will be dynamically prompted.

[0069] Optionally, based on process priority and current execution data, the execution priority corresponding to each process node is evaluated, including: pre-setting priority weights corresponding to each target priority, wherein the target priority includes process priority and current priority, and the current priority is obtained by matching the current execution data; obtaining data statistics results by performing data statistics on each historical execution data, so as to evaluate the historical meta-state of the business process model based on the data statistics results; and monitoring the task urgency of each current execution data, so as to evaluate the current meta-state of the business process model based on the monitoring results, wherein the data statistics results include historical data confidence and / or historical data volume. The confidence level of historical data is calculated based on the timeliness distribution and dispersion. If the historical meta-state and the current meta-state meet the first preset condition, the priority weight corresponding to the process priority is increased. The first preset condition includes that the historical meta-state is in a stable period and the current meta-state is in a normal execution state. If the historical meta-state and the current meta-state meet the second preset condition, the priority weight corresponding to the current priority is increased. The second preset condition includes that the historical meta-state is in an exploratory period and the current meta-state is in an emergency execution state. The target priority corresponding to any process node is weighted according to each priority weight to obtain the execution priority corresponding to the process node.

[0070] In some embodiments, the confidence level of historical data is determined by analyzing the time distribution of historical execution data. If most of the data is concentrated in the most recent week, it indicates good data timeliness and a high confidence level is assigned to the historical data. Conversely, if the data is evenly distributed over the past year, the confidence level of historical data is low. The confidence level of historical data is also determined by calculating the coefficient of variation of key performance indicators (e.g., node execution time). A small coefficient of variation indicates very stable historical execution performance and a high dispersion score, while a large coefficient of variation indicates large performance fluctuations and a low score. The historical data volume is extracted from the historical execution data. The historical data volume is the total number of completed instances of the process model over a past period (e.g., within one month).

[0071] In some embodiments, if the amount of historical data is greater than a preset threshold and the confidence level of the historical data is high, the historical meta-state is determined to be a "stable period state", indicating that historical experience has high reference value; if the amount of historical data is less than the threshold or the confidence level of the historical data is low, the historical meta-state is determined to be an "exploratory period state", indicating that the reference value of historical experience is limited.

[0072] In some embodiments, the proportion of currently running process instances with high task urgency is counted; if the proportion is lower than a preset threshold, the current meta-state is evaluated as "normal execution state", indicating that the current operation is stable and there are no large number of sudden urgent tasks; if the proportion is higher than the preset threshold, the current meta-state is evaluated as "emergency execution state", indicating that the execution process is facing a sudden high load or urgent business impact.

[0073] In some embodiments, if a "historical meta-state is in a stable period" and a "current meta-state is in a normal execution state" is detected, it indicates that historical experience is very reliable and there is no emergency at present, so the priority weight corresponding to the process priority is increased; if a "historical meta-state is in an exploratory period" and a "current meta-state is in an emergency execution state" is detected, it indicates that historical experience is insufficient to guide decision-making and the current situation is urgent, so the priority weight corresponding to the current priority is increased; if the first preset condition and the second preset condition are not met, the target priority corresponding to the process node is weighted according to the preset weight.

[0074] Optionally, after executing each process node in the business process model according to the process rules, the method further includes: detecting logical verification information of the business process model during execution, wherein the logical verification information includes at least one of logical relationship loops, trigger condition conflicts, and branch path unclosed; and generating correction suggestions corresponding to the business process model based on the detected logical verification information.

[0075] In some embodiments, at least one of the following in the process rules—logical relationship loops, trigger condition conflicts, and unclosed branch paths—is detected to reduce the risk of execution errors.

[0076] In some embodiments, the currently executed data is stored in a preset database and displayed through a visual dashboard, which includes a process topology diagram, an execution progress monitoring view, and a performance heatmap.

[0077] In some embodiments, the process topology diagram renders the topology of the business process model being executed in real time, with a layout consistent with the designer in the low-code platform. Unlike the static design diagram, this view is dynamic, where the executing process nodes are highlighted with a specific color (such as green), while completed nodes are displayed in gray. If a node fails to execute or encounters an exception, it will immediately turn red, possibly with a flashing effect, so that the user can notice it immediately.

[0078] In some embodiments, the execution progress monitoring view displays detailed information about running process instances in the form of a list or Gantt chart. Each row represents a process instance and lists the instance ID (Identifier), start time, current node, current handler, and key business data. This view supports real-time search, sorting, and filtering functions. Users can click on an instance to view its complete execution history, the handler and time of each node, and the current values ​​and historical change records of all process variables.

[0079] In some embodiments, the performance heatmap is an overlay view based on the process topology diagram. It renders a "heat" effect of varying color depth on each node and connection in the process topology diagram according to the key performance indicators in the current execution data, thereby intuitively revealing the performance bottleneck. The color of the node can be determined by the "average task waiting time" or the "current number of queued tasks". The longer the waiting time or the more queued tasks, the darker the color of the node, indicating that the node is a bottleneck in the process.

[0080] Combination Figure 3 As shown, this application provides an execution system for a business process model, including a low-code platform 301, an adaptive rule engine 302, a task execution module 303, and a task scheduling module 304.

[0081] The low-code platform 301 is used to obtain the business process model, which includes multiple process nodes.

[0082] The adaptive rule engine 302 is used to evaluate the process priority corresponding to each process node based on the historical execution data associated with each process node, so as to establish process rules between each process node according to the process priority. The historical execution data is obtained by collecting data when the process node is in the execution state.

[0083] The task execution module 303 is used to execute each process node in the business process model according to the process rules.

[0084] The task scheduling module 304 is used to respond to any process node being in the execution state by acquiring the current execution data of each process node through data acquisition, so as to evaluate the execution priority of each process node according to the process priority and the current execution data, and adjust the process rules in real time according to the execution priority.

[0085] The execution system employing the business process model provided in this application evaluates the process priority corresponding to each process node by using historical execution data associated with the process nodes. Based on the process priority, process rules are established, achieving the first layer of optimization. Then, during the execution of the business process model according to these rules, current execution data is collected, and combined with the process priority from the first layer of optimization, the process rules are optimized in real-time for the second layer. In this way, by establishing a two-layer optimization model for process rules using historical and current execution data, the first layer pre-evaluates process priorities based on historical execution data, optimizing the process rules before execution. The second layer combines real-time changes represented by current execution data to optimize the process rules during execution. This makes the formulation and optimization of business process rules more comprehensive and timely, avoiding decision-making delays or errors caused by relying on partial data in existing technologies. It ensures that process scheduling always selects the optimal path, achieving higher processing throughput and resource utilization, and improving the execution efficiency of the business process model.

[0086] This application also provides an electronic device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the above-described method.

[0087] Figure 4 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0088] like Figure 4As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage portion 408 into Random Access Memory (RAM) 403. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0089] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0090] The electronic device disclosed in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and enable communication between them. The memory is used to store computer programs, the communication interface is used for communication, and the processor and transceiver are used to run the computer programs, causing the electronic device to perform the various steps of the above method. The above description and drawings fully illustrate the embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operation may vary. Parts and subsamples of some embodiments may be included in or replace parts and subsamples of other embodiments. Moreover, the terminology used in this application is only for describing embodiments and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated subsamples, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other subsamples, wholes, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes the element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0091] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some sub-samples may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. Furthermore, the functional units in this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the systems, methods, and computer program products according to this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than those disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method of executing a business process model, characterized by, include: Obtain a business process model, wherein the business process model includes multiple process nodes; Based on the historical execution data associated with each process node, the process priority corresponding to each process node is evaluated, so as to establish process rules between each process node according to the process priority. The historical execution data is obtained by data collection when the process node is in the execution state. Execute each process node in the business process model according to the stated process rules; In response to any process node being in an execution state, the current execution data of each process node is acquired through data collection, and the execution priority corresponding to each process node is evaluated according to the process priority and the current execution data, and the process rules are adjusted in real time according to the execution priority.

2. The method of claim 1, wherein, Obtain the business process model, including: The user-inputted process node information is obtained through a preset low-code platform, and process nodes are constructed based on the process node information. The process node information includes at least one of node definition, task type, execution role, data flow method, and external interface.

3. The method of claim 1, wherein, Based on the historical execution data associated with each process node, the process priority corresponding to each process node is evaluated, including at least one of the following: If the historical execution data associated with the process node includes the node execution time, then the process priority corresponding to the process node is determined according to the node execution time and a negative correlation. If the historical execution data associated with the process node includes the growth frequency corresponding to the execution duration of the node, then the process priority corresponding to the process node is determined according to the negative correlation based on the growth frequency corresponding to the execution duration of the node. If the historical execution data associated with the process node includes the execution success rate, then the process priority corresponding to the process node is determined according to the execution success rate and a positive correlation. If the historical execution data associated with the process node includes the frequency of anomalies, then the process priority corresponding to the process node is determined according to the negative correlation based on the frequency of anomalies. If the historical execution data associated with the process node includes the growth frequency corresponding to the frequency of the anomaly, then the process priority corresponding to the process node is determined according to the negative correlation based on the growth frequency corresponding to the frequency of the anomaly. If the historical execution data associated with the process node includes resource utilization rate, then the process priority corresponding to the process node is determined according to the negative correlation based on the resource utilization rate. If the historical execution data associated with the process node includes task waiting time, then the process priority corresponding to the process node is determined according to the task waiting time and a positive correlation.

4. The method of claim 1, wherein, Based on the process priority, process rules are established between each process node, including: According to each process node, an initial rule is matched from a preset rule base between each process node, wherein the initial rule includes at least one of logical relationship, triggering condition, execution order, and branch path; The initial rules are adjusted according to the process priority to obtain the process rules between each process node.

5. The method of claim 1, wherein, Execute each process node in the business process model according to the stated process rules, including: Define any two process nodes as the first node and the second node, respectively. The process rules determine whether there is a dependency relationship between the first node and the second node, wherein the dependency relationship includes at least one of node dependency relationship, data dependency relationship and resource dependency relationship; If there is no dependency between the first node and the second node, the first node and the second node will be executed in parallel. If there is a dependency relationship between the first node and the second node, then the first node and the second node are executed sequentially according to the dependency relationship.

6. The method of claim 1, wherein, Based on the process priority and the current execution data, the execution priority corresponding to each process node is evaluated, including at least one of the following: Based on the process priority corresponding to the process node, the execution priority corresponding to the process node is determined according to the positive correlation. If the current execution data corresponding to the process node includes the task urgency level, then the execution priority corresponding to the process node is determined according to the task urgency level and a positive correlation. If the current execution data corresponding to the process node includes the user permission level, then the execution priority corresponding to the process node is determined according to the positive correlation based on the user permission level. If the current execution data corresponding to the process node includes the task waiting time, then the execution priority corresponding to the process node is determined according to the task waiting time and a positive correlation.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the process priority and the current execution data, the execution priority corresponding to each process node is evaluated, including: Each target priority is pre-set with its corresponding priority weight, wherein the target priority includes the process priority and the current priority, and the current priority is obtained by matching the current execution data; By performing data statistics on each of the historical execution data, data statistics results are obtained to evaluate the historical meta-state of the business process model based on the data statistics results. In addition, the task urgency of each of the current execution data is monitored to evaluate the current meta-state of the business process model based on the monitoring results. The data statistics results include historical data confidence and / or historical data volume. The historical data confidence is calculated based on the timeliness distribution and dispersion. If the historical meta-state and the current meta-state satisfy the first preset condition, then the priority weight corresponding to the process priority is increased, wherein the first preset condition includes the historical meta-state being a stable state and the current meta-state being a normal execution state. If the historical meta-state and the current meta-state satisfy the second preset condition, then the priority weight corresponding to the current priority is increased, wherein the second preset condition includes the historical meta-state being an exploration phase state and the current meta-state being an emergency execution state; The execution priority of any process node is obtained by weighting the target priority corresponding to each priority weight.

8. The method according to any one of claims 1 to 6, characterized in that, After executing each process node in the business process model according to the stated process rules, the method further includes: The logic verification information of the business process model during execution is detected, wherein the logic verification information includes at least one of logical relationship loop, trigger condition conflict and branch path not closed; Based on the detected logical verification information, correction suggestions are generated for the business process model.

9. An execution system of a business process model, characterized by, include: A low-code platform for acquiring a business process model, wherein the business process model includes multiple process nodes; An adaptive rule engine is used to evaluate the process priority corresponding to each process node based on the historical execution data associated with each process node, so as to establish process rules between each process node according to the process priority. The historical execution data is obtained by data collection when the process node is in the execution state. The task execution module is used to execute each process node in the business process model according to the process rules. The task scheduling module is used to respond to any process node being in an execution state by acquiring the current execution data of each process node through data collection, so as to evaluate the execution priority of each process node according to the process priority and the current execution data, and adjust the process rules in real time according to the execution priority.

10. An electronic device comprising: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the execution method of the business process model according to any one of claims 1 to 8.

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