Cloud workflow engine deployment device, workflow execution method, equipment and medium

Abstract

The invention discloses a cloud workflow engine deployment device, a workflow execution method, equipment and a medium, and belongs to the technical field of data processing. The method comprises the following steps: creating a workflow for processing a target service and a target engine function corresponding to the workflow; and when the workflow is triggered, calling the target engine function to execute the workflow. And obtaining execution result data of the workflow, and returning the execution result data to the target user associated with the workflow. Through the embodiment of the invention, each workflow can be configured with an independent target engine function, and the target engine function is started to execute the workflow only when the execution of the workflow is triggered, so that the task overhead is reduced. And task requirements of different tenants can be met.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, specifically relating to a cloud workflow engine deployment device, a workflow execution method, an electronic device, and a medium. Background Technology

[0002] In cloud computing environments, workflow engines are a key technology for achieving business automation and data processing. Traditional cloud workflow engines typically need to run continuously, incurring overhead even when no tasks are being executed. Furthermore, different tenants may have different concurrent task requirements, and existing deployment methods struggle to flexibly address these needs. Summary of the Invention

[0003] The purpose of this application is to provide a cloud workflow engine deployment device, workflow execution method, equipment, and medium that can solve problems such as high task execution overhead and high tenant concurrency requirements.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide an apparatus for deploying a cloud workflow engine, the apparatus comprising a workflow console module, a workflow execution engine function module, and a workflow execution event storage function module, wherein: The workflow console module is used to create workflows for processing target business and the target engine functions corresponding to those workflows. The workflow execution engine function module is used to call the target engine function to execute the workflow when the workflow is triggered; The workflow execution event storage function module is used to obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

[0005] Optionally, the device further includes a workflow triggering control module, wherein: The workflow triggering control module is used to trigger the execution of the workflow based on the function calculation trigger that has been created to trigger the execution of the workflow.

[0006] Optionally, the apparatus further includes a workflow metadata management function module, wherein: The workflow metadata management function module is used to store the metadata of the workflow and the target engine function when they are created.

[0007] Optionally, the workflow console module is used to control the workflow to communicate and call with FAAS, and to create the workflow engine function corresponding to the workflow based on the preset openapi.

[0008] Optionally, the workflow console module is used to trigger the execution of the workflow in response to an execution operation performed by a target user on the workflow; Optionally, the workflow console module is used to create function computation triggers corresponding to the workflow.

[0009] Optionally, the workflow execution engine module is used to determine the resource information corresponding to the workflow and allocate resources according to the resource information.

[0010] Secondly, embodiments of this application provide a workflow execution method, which... Create a workflow for processing the target business and the target engine function corresponding to the workflow; When the workflow is triggered, the target engine function is invoked to execute the workflow; Obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

[0011] Optionally, the step of creating a workflow for processing the target business and the target engine function corresponding to the workflow includes: Create a workflow for processing the target business, and create the corresponding workflow engine function based on the preset openapi.

[0012] Optionally, the step of creating a workflow for processing the target business and the target engine function corresponding to the workflow includes: Create a workflow to process the target business; The workflow is validated. If the verification passes, the target engine function corresponding to the workflow is created.

[0013] Optionally, it also includes: Determine whether the target engine function was created successfully; When the creation of the target engine data is determined to be successful, the metadata of the workflow and the target engine function is stored.

[0014] Optionally, it also includes: Create a function computation trigger to initiate the execution of the workflow; Obtain current scene data; Based on the function, a trigger is calculated to determine whether the current scene data matches the triggering event of the workflow; When the current scene data is determined to match the triggering event of the workflow, the workflow is triggered.

[0015] Optionally, it also includes: When the workflow is triggered, the resource information corresponding to the workflow is determined, and resources are allocated according to the resource information.

[0016] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0017] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0018] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0019] In this embodiment, a cloud workflow engine deployment device is provided. The workflow console module is used to create workflows for processing target business and corresponding target engine functions. The workflow execution engine function module is used to call the target engine function to execute the workflow when it is triggered. The workflow execution event storage function module is used to obtain the execution result data of the workflow and return it to the target user associated with the workflow. Thus, each workflow can be configured with an independent target engine function, which is only started when the workflow is triggered, thereby reducing task overhead and addressing the task requirements of different tenants. Attached Figure Description

[0020] Figure 1a This is a schematic diagram of the structure of a cloud workflow engine deployment device according to an embodiment of this application; Figure 1b This is a schematic diagram of the overall architecture of a workflow execution engine deployed based on FAAS in an embodiment of this application; Figure 2 This is a flowchart illustrating a workflow execution method in an embodiment of this application; Figure 3a This is a flowchart illustrating a workflow execution method in an embodiment of this application; Figure 3b This is a schematic diagram of a workflow creation process in one embodiment of this application; Figure 3c This is a schematic diagram of a workflow creation process in one embodiment of this application; Figure 4This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0023] The workflow execution provided in this application embodiment will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0024] Reference Figure 1a The diagram shows a schematic of a cloud workflow engine deployment device according to an embodiment of this application. The device includes a workflow console module 101, a workflow execution engine function module 102, and a workflow execution event storage function module 103.

[0025] Specifically, the workflow console module 101 can be used to create a workflow for processing target business and a target engine function corresponding to the workflow; the workflow execution engine function module 102 can be used to call the target engine function to execute the workflow when the workflow is triggered; the workflow execution event storage function module 103 can be used to obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

[0026] A cloud workflow engine is a fully managed serverless cloud service used in a cloud computing environment to coordinate, manage, and execute multiple distributed tasks. It allows users to design and orchestrate workflows through a visual interface or process definition language, organizing different services, API calls, or data processing steps into an ordered workflow, reliably coordinating task execution according to the pre-defined steps, and tracking the state transitions of each task. It also provides error retry and capture capabilities, improving the fault tolerance of applications based on cloud workflow orchestration.

[0027] Serverless FaaS: Function as a Service. Serverless FaaS is a cloud computing service model that allows developers to write and deploy code without managing the underlying servers or runtime environment. In this model, the cloud service provider is responsible for the maintenance, expansion, and management of the servers. Developers only need to focus on writing code and implementing functionality, without worrying about server configuration, maintenance, and expansion.

[0028] In practical applications, a workflow is a series of processes used to handle a set target business, and the target engine function is a function that can execute the workflow. The workflow console module in the cloud workflow engine deployment device can define and create workflows. Common cloud workflow definitions include cloud vendor-defined workflow description languages ​​such as Amazon's Step Function Workflow Description Language (Amazon States Languages), Alibaba Cloud's FlowDefinition Language, and the Serverless Workflow DSL defined by CNCF.

[0029] After defining the workflow, you can save and complete the workflow creation. During workflow creation, metadata such as workflow ID, workflow engine function ID, workflow definition, workflow execution timeout, and workflow execution quota are generated. The workflow ID is a unique identifier for each created workflow.

[0030] Metadata generated during the creation of workflow and workflow engine functions can be stored for later use. For example, metadata can be stored in the workflow metadata management function module.

[0031] In practical applications, the workflow execution engine module 102 can utilize functions created by the workflow console module in the instance area via the Function Compute OpenAPI when a user creates a workflow. Each workflow ID can be bound to a specific workflow execution engine function. All workflow engine functions share the same code; the difference lies in their dependency on environment variables during startup and runtime. These environment variables can be used to identify the specific function ID. Therefore, the user is unaware of the existence of this user module. In short, the workflow execution module can be deployed via functions.

[0032] The workflow execution engine module can also be configured with input / output conversion filtering, task retries, and other mechanisms, which can then be relied upon to ensure the reliable execution of the workflow.

[0033] Input-output transformation and filtering mechanisms refer to transforming, filtering, or enhancing input data at each node (or step) of the workflow, and then passing the processed data to the next node. This can include data mapping, data transformation, data filtering, and data enhancement. Specifically: Data mapping can be used to map the output data of the previous node to the input parameters required by the current node.

[0034] Data transformation can be changing the format or structure of data, such as converting from JSON to XML, or converting data units.

[0035] Data filtering can involve selecting only a portion of the data to pass to the next node, or filtering out some data based on conditions.

[0036] Data augmentation can involve adding additional data, such as enriching data by querying a database or calling an external service.

[0037] The task retry mechanism in a workflow engine is a key component for ensuring process reliability. When a task fails, the retry mechanism can automatically re-execute the task according to a predefined strategy to increase the probability of eventual success.

[0038] In practical applications, the following core elements can be combined to design a task retry mechanism.

[0039] Retry conditions: Under what circumstances should a retry be made, such as when the task execution throws an exception or returns a specific error code.

[0040] Retry strategy: including number of retries, retry interval (fixed interval, exponential backoff, etc.), maximum retry time, etc.

[0041] Retry context: What context information needs to be maintained during a retrieval? For example, the number of retries already made, the previous error message, etc.

[0042] Failure handling: What should be done when the maximum number of retries is reached? Failures can be logged, alarms can be triggered, or backup tasks can be executed.

[0043] In a workflow engine, each node has inputs and outputs. A node receives the output of the previous node as input, then processes it internally (perhaps by calling an API or executing code) to produce output. Transformation and filtering mechanisms can then process the data before and after node execution.

[0044] The workflow execution engine in this embodiment can be deployed as functions, naturally supporting on-demand execution and event-driven execution. Furthermore, the binding of workflow execution engine functions to specific workflow IDs ensures that workflow executions do not interfere with each other, and resource isolation between functions provides a certain level of resource and security assurance.

[0045] In the workflow execution engine function module 102, when the workflow execution is triggered, the workflow metadata can be obtained, initialized using the metadata, and then the workflow can be executed.

[0046] In this embodiment of the application, the workflow execution event storage function module 103 can be used to obtain the workflow execution result. The workflow execution result can include states such as execution in progress, execution success, execution failure, execution timeout, and execution termination. It also includes the execution start time and termination time of each task during the workflow execution process, as well as the execution input and output, which can also be deployed in the instance area of ​​the device in a function calculation manner.

[0047] In one embodiment of this application, the workflow console module can be used to control the workflow to communicate and call FAAS (Function as a Service), and create the workflow engine function corresponding to the workflow based on a preset openapi.

[0048] In practical applications, FAAS (Function as a Service) can specifically be Serverless FaaS. As an emerging cloud computing service model, Serverless FaaS has become a focus of industry attention. Serverless FaaS allows developers to deploy applications as a series of independent functions that execute only when called and are billed based on execution time. This model is characterized by event-driven execution, on-demand execution, automatic scaling, and pay-as-you-go pricing. Serverless FaaS is particularly suitable for asynchronous concurrency scenarios, scenarios where components can be deployed and scaled independently, and scenarios with sudden spikes or unpredictable service usage.

[0049] In this embodiment of the application, the workflow engine can be designed as a FaaS function, with each FaaS function uniquely corresponding to a workflow and each FaaS function responsible for the execution of the entire workflow, thereby ensuring effective isolation of workflow execution.

[0050] Meanwhile, based on FaaS's inherent tenant isolation and dynamic resource allocation mechanisms, on-demand resource allocation and cost optimization can be achieved, improving the system's flexibility and reliability and meeting the concurrent task requirements of different tenants in a cloud computing environment.

[0051] In practical applications, a workflow engine function can also be created based on a pre-defined OpenAPI specification (usually a JSON or YAML file describing the API endpoints, parameters, responses, etc.). This workflow engine function can automatically generate or execute the corresponding workflow according to the OpenAPI definition.

[0052] The technical solutions described in this application embodiment can achieve the following effects: Improved resource utilization: Workflows are triggered on demand, consuming resources only during execution and incurring no additional costs during off-peak periods, significantly improving resource utilization and reducing costs.

[0053] Improved execution efficiency: The FaaS platform automatically scales up and down, dynamically adjusting resources based on request volume to achieve millisecond-level response times, ensuring stable service in high-concurrency scenarios and improving execution efficiency.

[0054] Enhanced system stability: The impact of workflow failures is limited, facilitating rapid location and recovery. The execution of a single workflow is limited to only one workflow function instance or multiple corresponding split workflow function instances, without affecting other workflows, thus enhancing system reliability.

[0055] Reduced operation and maintenance costs: Users can focus on business logic implementation without having to worry about the details of underlying server operation and maintenance, thereby reducing the difficulty and workload of operation and maintenance and improving development efficiency.

[0056] In one embodiment of this application, the workflow console module can also be configured with a manual execution function, allowing users to choose to trigger the workflow themselves. During the manual execution function, the workflow console module can respond to the target user's execution operation on the workflow, triggering the execution of the workflow. That is, the workflow console module can be configured to manually execute workflow processes.

[0057] In one embodiment of this application, the workflow console module can provide a visual display to the user, such as a list of workflows created by the current user and the execution details of the corresponding workflows.

[0058] In one embodiment of this application, the workflow console module is used to create a Function Compute trigger corresponding to the workflow. The Function Compute trigger is a trigger used to automatically trigger the execution of the workflow. Specifically, the Function Compute trigger can be an HTTP trigger, a timed trigger, or a message queue type trigger, such as a Kafka trigger, a RocketMQ trigger, or a cloud-native gateway trigger.

[0059] In one embodiment of this application, the cloud workflow engine deployment device may further include a workflow trigger control module 104, which can be used to trigger the execution of the workflow based on the function calculation trigger that has been created for triggering the execution of the workflow.

[0060] In practical applications, the workflow trigger control module 104 can be configured with function computation triggers for controlling the free execution of workflows, such as HTTP triggers, timed triggers, and message queue type triggers, such as Kafka triggers, RocketMQ triggers, cloud-native gateway triggers, etc.

[0061] In practical applications, the workflow console can access the workflow trigger control module 104 via HTTP triggers, thereby returning the workflow execution results to the user via API. These results can then be displayed visually to the user on the front end. The user is the person associated with the workflow.

[0062] In one embodiment of this application, the apparatus may further include a workflow metadata management function module 105, which can be used to store metadata of the workflow and the target engine function at the time of creation, such as workflow definition, workflow ID, workflow execution timeout, and workflow execution limit.

[0063] In one embodiment of this application, the workflow execution engine module can be used to determine the resource information corresponding to the workflow and allocate resources according to the resource information.

[0064] Reference Figure 1b The diagram illustrates the overall architecture of a workflow execution engine deployed based on FAAS in an embodiment of this application: Common service resource management methods for cloud computing PaaS components: The national region is responsible for deploying PaaS components and providing service entry points; the instance region mainly consists of IDC resource pools in various regions, which are used for the resources actually used by cloud computing users.

[0065] This application embodiment, combining the resource management methods described above, mainly includes five modules in the national and instance regions: a workflow console module, a workflow metadata management function module, a workflow execution engine function module, a workflow execution event storage function module, and a workflow trigger control module. The functionality, workflow, and implementation details of each component are described below: (a) Workflow Console Module: The workflow console module is primarily deployed nationwide. Its main function is to allow users to define and create workflows. Common cloud workflow definitions include cloud vendor-defined workflow description languages ​​such as Amazon's Step Function Workflow Description Language (Amazon States Language), Alibaba Cloud's FlowDefinition Language, and the CNCF-defined Serverless Workflow DSL. After defining the workflow, it can be saved to complete workflow creation. Workflow creation requires communication with FAAS (Software as a Service) and the creation of the workflow engine function based on OpenAPI. Each workflow corresponds to one workflow engine function. Furthermore, workflow creation requires saving information such as the workflow ID, workflow engine function ID, workflow definition, workflow execution timeout, and workflow execution limits to the workflow metadata management module. The workflow ID is a unique identifier for each created workflow.

[0066] In addition, the workflow console provides access to manually trigger workflow execution, create workflow scheduling triggers, and displays a list of workflows created by the current user, as well as the execution details of the corresponding workflows.

[0067] (ii) Workflow metadata management function module: The workflow metadata management module is primarily deployed in the instance area, defined and deployed via Function Compute. When a workflow is triggered, the workflow execution engine retrieves the workflow definition, workflow ID, workflow execution timeout, and workflow execution limit using the function ID. The workflow engine function can then execute the workflow based on this information. This function can provide service access and requests by binding to an HTTP trigger, or it can be accessed by the workflow execution engine via intranet access between functions. Furthermore, this workflow metadata management module requires additional persistent storage middleware such as MySQL or PostgreSQL to store workflow metadata and provide structured query capabilities.

[0068] (III) Workflow Execution Engine Function Module: The workflow execution engine function module is a function created in the instance area by the workflow console module through the Function Compute OpenAPI when a user creates a workflow. The existence of this user module is not apparent to the workflow user. Simply put, the workflow execution module is deployed through functions. Each workflow ID is bound to a workflow execution engine function. All workflow engine functions have identical code; the difference lies in their dependency on environment variables during startup and runtime, which identify the specific function ID. When triggered, the module retrieves the workflow definition, workflow ID, workflow execution timeout, and workflow execution limit from the workflow metadata management function module via the inter-function intranet request, thus completing the workflow engine module initialization. It then executes the workflow according to the definition and expected scheduling method, synchronously storing the input and output of each task and the workflow's input and output in the workflow execution time storage function.

[0069] In addition, the workflow execution engine module also has mechanisms such as input / output conversion filtering and task retries to ensure the reliable execution of the workflow.

[0070] The workflow execution engine is deployed as functions, naturally supporting on-demand execution and event-driven execution. Furthermore, the binding of workflow execution engine functions to specific workflow IDs ensures that workflows do not interfere with each other, and resource isolation between functions provides a certain level of resource and security assurance. Additionally, additional resources, such as CPU and memory, can be allocated to individual workflows at the workflow level by increasing function quotas to meet execution demands. (iv) Workflow execution event storage function module: The workflow execution event storage module is primarily used to retrieve workflow execution results and is deployed in the instance area using the same function computation approach. It includes workflow execution statuses such as: executing, successful, failed, timed out, and terminated. It also includes the start and end times of each task during workflow execution, as well as the input and output. The workflow console can access this module via HTTP triggers, returning the workflow execution results to the user via API for visualization on the front end.

[0071] (v) Workflow Trigger Control Module: The workflow trigger control module is mainly the driver management module for workflow trigger execution. Since the workflow execution engine is deployed through functions, it naturally supports function computing triggers, such as HTTP triggers, timed triggers, and message queue type triggers, such as Kafka triggers, RocketMQ triggers, and cloud-native gateway triggers. Based on this, it basically meets the needs of scenarios where workflows are triggered and executed based on events. Furthermore, triggers related to the event bus can be connected. Deploying the workflow execution engine module based on FaaS can support event-triggered workflow execution at minimal cost, and can be extended to event-driven scenarios based on the event bus.

[0072] Existing cloud workflow engines typically require continuous operation, and their deployment architecture primarily follows two technical approaches: Centralized engine: A single node processes all tasks, which poses a risk of single-end failure. In cloud computing, which is multi-tenant, it cannot meet the requirements of resource isolation and risk isolation.

[0073] Distributed containerized deployment: The workflow master allocates execution units to the nodes of the process, and Kubernetes is used for scheduling, allocation, execution of tasks, and scaling of task nodes. This solves the risk of resource isolation, but the container startup latency is high (3-10s) and basic resources still need to be reserved. It cannot be executed on demand according to event-driven principles.

[0074] In this application embodiment, based on the problems existing in the two current technical routes, under the development trend of cloud computing Serverless, FaaS can be used as the deployment foundation to support the advantages of automatic elastic scaling, event triggering and on-demand execution, and multi-tenant resource isolation, thereby solving the shortcomings of traditional technical solutions such as slow startup speed, resource waste, and ineffective isolation of multi-tenant resources.

[0075] The specific technical problems that can be solved and the technical effects that can be achieved by the above embodiments of this application are as follows: (1) Technical issues: In terms of resource management: resident process services require pre-allocation of fixed computing resources, which leads to idle and wasted resources during off-peak periods. Resources from different workflows can interfere with each other, which can easily cause performance problems.

[0076] In terms of execution efficiency: When faced with fluctuations in business traffic, traditional services require manual configuration for scaling up and down, which is slow, difficult to respond to sudden high-concurrency requests, and prone to performance bottlenecks. The workflow engine resides in memory, resulting in high resource consumption and impacting system efficiency.

[0077] In terms of fault isolation: In traditional deployment methods, workflow components are tightly coupled, and a single point of failure can easily spread to the entire system, affecting multiple workflows, making it difficult to locate and recover from, and increasing the operational burden.

[0078] In terms of simplifying operations and maintenance: Traditional service operations and maintenance are complex, requiring attention to server maintenance, load balancing configuration, software updates, etc., which requires a highly professional team, has high operation and maintenance costs, and low development efficiency.

[0079] (2) Technical effects: Improved resource utilization: Workflows are triggered on demand, consuming resources only during execution and incurring no additional costs during off-peak periods, significantly improving resource utilization and reducing costs.

[0080] Improved execution efficiency: The FaaS platform automatically scales up and down, dynamically adjusting resources based on request volume to achieve millisecond-level response times, ensuring stable service in high-concurrency scenarios and improving execution efficiency.

[0081] Enhanced system stability: The impact of workflow failures is limited, facilitating rapid location and recovery. The execution of a single workflow is limited to only one workflow function instance or multiple corresponding split workflow function instances, without affecting other workflows, thus enhancing system reliability.

[0082] Reduced operation and maintenance costs: Users do not need to worry about the details of underlying server operation and maintenance, but can focus on the implementation of business logic, reducing the difficulty and workload of operation and maintenance and improving development efficiency.

[0083] Reference Figure 2 The diagram illustrates a workflow execution method according to an embodiment of this application, which may specifically include the following steps: Step S201: Create a workflow for processing the target business and the target engine function corresponding to the workflow; In practical applications, a workflow is a series of processes used to handle a set target business, and the target engine function is a function that can execute the workflow. The workflow console module in the cloud workflow engine deployment device can define and create workflows. Common cloud workflow definitions include cloud vendor-defined workflow description languages ​​such as Amazon's Step Function Workflow Description Language (Amazon States Languages), Alibaba Cloud's FlowDefinition Language, and the Serverless Workflow DSL defined by CNCF.

[0084] In practical applications, to enable on-demand or automatic execution of workflows, you can first create the workflow and the corresponding target engine function.

[0085] Step S202: When the workflow is triggered, the target engine function is invoked to execute the workflow; In this embodiment of the application, a trigger event for the workflow can be preset. When the trigger event is activated, the workflow is triggered, and the target engine function can be called to execute the workflow.

[0086] Step S203: Obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

[0087] The execution results can include statuses such as execution in progress, execution successful, execution failed, execution timeout, and execution terminated. They also include the start and end times of each task during the workflow execution process, as well as the inputs and outputs of the execution. These can also be deployed in the instance area of ​​the device in a function calculation manner.

[0088] In one embodiment of this application, the step of creating a workflow for processing the target business and a target engine function corresponding to the workflow includes: creating a workflow for processing the target business, and creating a workflow engine function corresponding to the workflow based on a preset openapi.

[0089] In one embodiment of this application, it can be determined whether the target engine function has been successfully created; when it is determined that the target engine data has been successfully created, the metadata of the workflow and the target engine function is stored.

[0090] In one embodiment of this application, a function computation trigger is created to trigger the execution of the workflow; Obtain current scene data; calculate a trigger based on the function to determine whether the current scene data matches the trigger event of the workflow; if the current scene data matches the trigger event of the workflow, trigger the workflow.

[0091] In one embodiment of this application, when the workflow is triggered, the resource information corresponding to the workflow is determined, and resources are allocated according to the resource information.

[0092] In this embodiment, a workflow for processing target business and a corresponding target engine function are created. When the workflow is triggered, the target engine function is called to execute the workflow. The execution result data of the workflow is obtained and returned to the target user associated with the workflow. This allows each workflow to be configured with an independent target engine function, and the target engine function is only started when the workflow is triggered, thus reducing task overhead. Furthermore, it can meet the task requirements of different tenants.

[0093] Reference Figure 3a The diagram illustrates a workflow execution method according to an embodiment of this application, which may specifically include the following steps: Step S301: Create a workflow for processing the target business; Step S302: Verify the workflow; After creating a workflow, it can be validated. Workflow validation is a crucial step in ensuring that the workflow definition is correct, secure, and reliable. Specifically, validation can be performed according to the established validation process to ensure that the workflow can be executed.

[0094] For example, the verification methods that can be set include any one or more of the following: semantic business layer verification, runtime layer verification, and verification with the structure layer and the basic layer.

[0095] Step S303: When the verification passes, create the target engine function corresponding to the workflow.

[0096] Step S304: When the workflow is triggered, the target engine function is invoked to execute the workflow; Step S305: Obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

[0097] In this embodiment, a workflow for processing target business is created; the workflow is validated; upon successful validation, a target engine function corresponding to the workflow is created; when the workflow is triggered, the target engine function is invoked to execute the workflow. The execution result data of the workflow is obtained and returned to the target user associated with the workflow. This embodiment enables each workflow to be configured with an independent target engine function, and the target engine function is only started when the workflow is triggered, thus reducing task overhead. It can also meet the task requirements of different tenants. Furthermore, by validating the created data, it can prevent anomalies during the creation process, which could lead to the inability to subsequently apply the created data workflow and target engine function.

[0098] Reference Figure 3b This document illustrates a workflow creation process according to an embodiment of the present application, which may include the following steps: Step S11: The user creates a workflow; Step S12, Workflow definition verification; Step S13, Workflow execution engine function construction; Step S14: Determine whether the creation was successful; Step S15: After successful creation, clean up and save the workflow metadata; Step S16: Determine whether the save was successful; Step S17: Workflow created successfully.

[0099] If the engine function fails to create or the workflow fails to create, then the current workflow creation is determined to have failed.

[0100] Reference Figure 3c This document illustrates a workflow creation process according to an embodiment of the present application, which may include the following steps: Step S21: Create workflow engine function triggers; For example, HTTP triggers, timed triggers, and message queue type triggers, such as Kafka triggers, RocketMQ triggers, and cloud-native gateway triggers.

[0101] Step S22, the event is triggered; If the current scene contains a preset trigger event, then the arrival of the trigger event can be determined.

[0102] Step S23: Distribute the time to the corresponding workflow and execute the engine function; Step S24: Obtain the workflow definition from the workflow metadata module; Step S25: The workflow engine executes the workflow and synchronizes the results to the time storage module.

[0103] It should be noted that the workflow execution method provided in this application embodiment can be executed by a cloud workflow engine deployment device, or a control module in the cloud workflow engine deployment device for executing the method of loading workflow execution. This application embodiment uses the execution of the method of loading workflow execution by a cloud workflow engine deployment device as an example to illustrate the workflow execution method provided in this application embodiment. The cloud workflow engine deployment device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc., and this application embodiment does not specifically limit the scope.

[0104] The cloud workflow engine deployment device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0105] The cloud workflow engine deployment device provided in this application embodiment can achieve... Figures 1a to 3c The various processes implemented by the cloud workflow engine deployment device in the method embodiment will not be described again here to avoid repetition.

[0106] Optionally, this application embodiment also provides an electronic device, including a processor 1010, a memory 1009, and a program or instructions stored in the memory 1009 and executable on the processor 1010. When the program or instructions are executed by the processor 1010, they implement the various processes of the above-described workflow execution method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0107] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0108] Figure 4 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application. The electronic device 1000 includes, but is not limited to, components such as: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010. The memory 1009 includes application programs and an operating system; the user input unit 1007 may include a touch panel 10071 and other input devices 100072; the input unit 1004 may include an image processor 10041 and a microphone 10042; and the display unit 1006 may include a display panel 10061.

[0109] Those skilled in the art will understand that the electronic device 1000 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1010 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here. This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described workflow execution method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0110] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0111] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described workflow execution method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0112] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0113] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0115] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A cloud workflow engine deployment apparatus, characterized by, The device includes a workflow console module, a workflow execution engine function module, and a workflow execution event storage function module, wherein: The workflow console module is used to create workflows for processing target business and the target engine functions corresponding to those workflows. The workflow execution engine function module is used to call the target engine function to execute the workflow when the workflow is triggered; The workflow execution event storage function module is used to obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

2. The apparatus according to claim 1, characterized in that, The device further includes a workflow triggering control module, wherein: The workflow triggering control module is used to trigger the execution of the workflow based on the function calculation trigger that has been created to trigger the execution of the workflow.

3. The apparatus according to claim 1, characterized in that, The device also includes a workflow metadata management function module, wherein: The workflow metadata management function module is used to store the metadata of the workflow and the target engine function when they are created.

4. The apparatus according to claim 1, characterized in that, The workflow console module is used to control the workflow to communicate and call with FAAS, and to create the workflow engine function corresponding to the workflow based on the preset openapi.

5. The apparatus according to claim 1, characterized in that, The workflow console module is used to trigger the execution of the workflow in response to the target user's execution operation on the workflow.

6. The apparatus according to claim 1, characterized in that, The workflow console module is used to create function calculation triggers corresponding to the workflow.

7. The apparatus according to claim 1, characterized in that, The workflow execution engine module is used to determine the resource information corresponding to the workflow and allocate resources according to the resource information.

8. A workflow execution method, characterized in that, The method includes: Create a workflow for processing the target business and the target engine function corresponding to the workflow; When the workflow is triggered, the target engine function is invoked to execute the workflow; Obtain the execution result data of the workflow and return the execution result data to the target user associated with the workflow.

9. The method according to claim 8, characterized in that, The creation of the workflow for processing the target business and the target engine function corresponding to the workflow includes: Create a workflow for processing the target business, and create the corresponding workflow engine function based on the preset openapi.

10. The method according to claim 9, characterized in that, The creation of the workflow for processing the target business and the target engine function corresponding to the workflow includes: Create a workflow to process the target business; The workflow is validated. If the verification passes, the target engine function corresponding to the workflow is created.

11. The method according to claim 10, characterized in that, Also includes: Determine whether the target engine function was created successfully; When the creation of the target engine data is determined to be successful, the metadata of the workflow and the target engine function is stored.

12. The method according to claim 8, characterized in that, Also includes: Create a function computation trigger to initiate the execution of the workflow; Obtain current scene data; Based on the function, a trigger is calculated to determine whether the current scene data matches the triggering event of the workflow; When the current scene data is determined to match the triggering event of the workflow, the workflow is triggered.

13. The method according to claim 8, characterized in that, Also includes: When the workflow is triggered, the resource information corresponding to the workflow is determined, and resources are allocated according to the resource information.

14. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the workflow execution method as described in any one of claims 8-13.

15. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the workflow execution method as described in any one of claims 8-13.

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