Metadata-based process arrangement method and device, computer equipment and medium
By acquiring, preprocessing and standardizing metaservice data, and using Activiti tools to build a dynamic process model for orchestration, the problem of low process orchestration in the existing technology is solved, and automated process orchestration and improved execution performance is achieved.
Patent Information
- Application Number
- CN202411943670.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-05-23
AI Technical Summary
The existing automated process orchestration technology fails to make full use of meta-service data, resulting in low process design and execution efficiency and difficulty in adapting to rapidly changing business needs.
By obtaining metaservice data of multiple services from the preset database, preprocessing and JSON standardization processing are performed, and saving them in MongoDB. Then, use the Activiti tool to build a dynamic process model and arrange it to obtain the process orchestration results.
It realizes automatic process orchestration when facing multi-tenants, multi-environments and complex scenarios, improving process execution efficiency and performance.
Smart Images
Figure CN120029607A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present application relate to the field of metadata processing technology, and in particular, to a metadata-based process scheduling method, apparatus, computer equipment, and medium. Background Art
[0002] In the era of big data, enterprises and organizations are facing the challenges of storing, managing and analyzing massive amounts of data. Meta-service data, as data used to describe business data, is crucial to helping machine or database administrators understand the content of business data, promote business data sharing and support automated decision-making. However, existing automated process orchestration technologies fail to fully utilize meta-service data, resulting in low efficiency in process design and execution, and difficulty in adapting to rapidly changing business needs.
[0003] At present, database administrators usually need to write scripts to identify abnormal business data. Although this method is simple and does not require additional system deployment, manually written scripts have poor maintainability, are prone to errors, and are difficult to monitor the subsequent business data rectification, which is prone to the problem of abnormal business data identification errors. Alternatively, another way is to set rules and regulations to limit the business data entered by database administrators. Although this method can prevent the entry or generation of abnormal or defective data to a certain extent, due to the objective differences in skill levels, cognitive differences, and sense of responsibility of database administrators, it may still cause problems with the quality of the business data finally entered. Therefore, in the face of the need to verify thousands of data quality rules, the existing method that relies on manual intervention is no longer suitable for current needs. Summary of the invention
[0004] In response to the above-mentioned deficiencies or shortcomings, the present application provides a metadata-based process orchestration method, apparatus, computer equipment and medium, which can automatically perform process orchestration when facing multi-tenants, multiple environments and complex scenarios, thereby improving process execution efficiency and performance.
[0005] According to a first aspect, the present application provides a metadata-based process orchestration method, the method comprising:
[0006] Obtaining meta-service data of multiple businesses from a preset database, where the meta-service data is data used to describe process use case data of corresponding businesses, where the process use case types of multiple businesses include office management, supply chain management, and customer service management of an enterprise or organization;
[0007] According to the set process arrangement requirements, preprocess the corresponding parameters of each meta-service data. The preprocessing operation includes editing, converting and obtaining values of parameters.
[0008] Perform JSON standardization on each meta-service data after preprocessing;
[0009] Save each meta-service data after JSON standardization processing to the preset distributed document storage database MongoDB (Mongo Database);
[0010] Based on the meta-service data in MongoDB, use the preset Activiti tool to build a dynamic process model;
[0011] Each meta-service data in the dynamic process model is orchestrated to obtain a process orchestration result; wherein the orchestration method corresponding to each meta-service data is determined by its corresponding system process or application requirement.
[0012] In some embodiments, the preset database is a relational database management system MySQL or MongoDB deployed in a multi-tenant cloud platform environment, and the process orchestration result includes a plurality of process use case data that have completed meta-service data format or structure conversion orchestration; after obtaining the process orchestration result, the method further includes:
[0013] In response to a target business trigger instruction from a user, executing a first process use case corresponding to the target business;
[0014] Acquire first process use case data corresponding to the first process use case from the cloud platform environment, and convert the first process use case data into a business process modeling notation (BPMN) XML standard format;
[0015] According to the first process use case data converted into the BPMN XML standard format, a business orchestration engine SmartEngine is created to execute the target business logic of the first process use case, obtain the process use case processing result of the first process use case and save it to a preset database.
[0016] In some embodiments, creating a business orchestration engine SmartEngine to execute the target business logic of the first process use case includes:
[0017] Parse the first process use case data converted into the BPMN XML standard format into the internal use case model of SmartEngine, and define context data for the internal use case model;
[0018] Create new second process use cases through SmartEngine’s API based on the internal use case model with defined context data;
[0019] The second process use case is started by sending a start event to the second process use case, and the corresponding service node processing, gateway node logic determination and event node timing process are executed;
[0020] The process case processing result of the second process case is used as the process case processing result of the first process case and is saved in a preset database.
[0021] In some embodiments, when executing the corresponding service node processing, gateway node logic determination and event node timing process, the method further includes:
[0022] If any of the processes of service node processing, gateway node logic determination, and event node timing is abnormal, the abnormal data and its context-related data will be saved in a preset database.
[0023] In some embodiments, the process of service node processing is executed by an HSF (Hazardous Substance Free) executor called from the cloud platform environment, and the application context environment appContext in the HSF executor also includes a pre-processor and a post-processor;
[0024] The pre-processor is used to splice the parameters input to the service node, verify the validity of the parameters, prepare the environment required for the service node processing, and send a processing request to the post-processor;
[0025] The postprocessor starts running after the service node is processed to verify whether the output result of the service node processing meets expectations; if the output result meets expectations, the output result is saved to the database; or, if the output result is abnormal and does not meet expectations, a rollback operation is performed on the abnormal output result.
[0026] In some embodiments, the Activiti tool is installed with an Activiti Modeler tool; based on each meta-service data in MongoDB, a dynamic process model is constructed using a preset Activiti tool, including:
[0027] Design the initial flow chart using the Activiti Modeler tool;
[0028] Define multiple nodes in the initial flow chart according to the business requirements corresponding to multiple businesses, each node being one of the start events, user tasks and gateways;
[0029] Set up flow paths between nodes based on the relationships between each meta-service data, and set the order and conditions of connections for each node;
[0030] Define the variables required for each decision point for the current initial flowchart according to the decision points in each meta-service data;
[0031] Based on the process branches and exclusive gateways existing in each meta-service data, corresponding conditional expressions are set for the current initial flowchart to build a dynamic process model.
[0032] In some embodiments, each meta-service data in the dynamic process model is extracted from the dynamic process model using a set warehouse technology ETL (Extract, Transform, Load) tool; each meta-service data in the dynamic process model is arranged, including:
[0033] According to the predefined data mapping relationship and conversion rules, the format or structure conversion arrangement is performed on each meta-service data in the dynamic process model.
[0034] According to a second aspect, the present application provides a metadata-based process orchestration device, the process orchestration device comprising:
[0035] A meta-service data acquisition module is used to acquire meta-service data of multiple businesses from a preset database. Meta-service data is data used to describe process use case data of corresponding businesses. The process use case types of multiple businesses include office management, supply chain management, and customer service management of an enterprise or organization.
[0036] The parameter preprocessing module is used to perform preprocessing on the corresponding parameters of each meta-service data according to the set process arrangement requirements. The preprocessing operation includes editing, converting and obtaining values of the parameters.
[0037] JSON standardization module, used to perform JSON standardization on each meta-service data after preprocessing;
[0038] The metadata storage module is used to save each meta-service data after JSON standardization processing into the preset distributed document storage database MongoDB;
[0039] The process model building module is used to build a dynamic process model based on the various meta-service data in MongoDB using the preset Activiti tool;
[0040] The dynamic process orchestration module is used to orchestrate each meta-service data in the dynamic process model to obtain a process orchestration result; wherein the orchestration method corresponding to each meta-service data is determined by its corresponding system process or application requirement.
[0041] According to a third aspect, the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the metadata-based process orchestration methods in the above-mentioned embodiments are implemented.
[0042] According to a fourth aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes, the steps of any one of the metadata-based process orchestration methods in the above-mentioned embodiments are implemented.
[0043] The metadata-based process orchestration method of the above embodiment can be applied to the cloud platform. The cloud platform first obtains the meta-service data of multiple businesses from the preset database, and then pre-processes the corresponding parameters of each meta-service data according to the set process orchestration requirements, and performs JSON standardization on each meta-service data after pre-processing. Then, each meta-service data after JSON standardization is saved in the preset distributed document storage database MongoDB, and based on each meta-service data in MongoDB, a dynamic process model is constructed using the preset Activiti tool. Finally, each meta-service data in the dynamic process model is orchestrated to obtain a process orchestration result. Through the steps of the above method, it is possible to automatically perform process orchestration when facing multi-tenants, multiple environments and complex scenarios, thereby improving process execution efficiency and performance. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] Figure 1 A schematic diagram of an application environment of a metadata-based process orchestration method in one or more embodiments of the present application;
[0045] Figure 2 A flowchart of a metadata-based process orchestration method in one or more embodiments of the present application;
[0046] Figure 3 A flowchart of a method for starting SmartEngine to execute business logic and save processing results in one or more embodiments of the present application;
[0047] Figure 4 A business process diagram executed after completing preliminary process arrangement in one or more embodiments of the present application;
[0048] Figure 5 A flowchart of a method for creating a SmartEngine to execute a target business logic of a first process use case in one or more embodiments of the present application;
[0049] Figure 6 A flowchart of a method for building a dynamic process model using a preset Activiti tool in one or more embodiments of the present application;
[0050] Figure 7 A schematic diagram of a meta-service business architecture in one or more embodiments of the present application;
[0051] Figure 8 A structural diagram of a metadata-based process orchestration device in one or more embodiments of the present application;
[0052] Fig. 9 This is a diagram of the internal structure of a computer device in one or more embodiments of the present application. DETAILED DESCRIPTION
[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0054] This application provides a metadata-based process scheduling method, which is applied to Figure 1 In the application environment shown, the cloud server (102) obtains the meta-service data of multiple services from the preset database (103) to implement the steps of the metadata-based process orchestration method of the present application. Then the user terminal (101) sends a target service trigger instruction to the cloud server (102), so that the cloud server (102) executes the first process use case corresponding to the target service. Finally, the cloud server (102) saves the process use case processing result to the preset database (103) and / or feeds back to the user terminal (101).
[0055] In some exemplary embodiments of the present application, Figure 2 As shown in the figure, a metadata-based process orchestration method is provided, which can be applied to Figure 1 The cloud server in the example is used as an example to illustrate the process, including the following steps:
[0056] Step 201: Obtain meta-service data of multiple businesses from a preset database. Meta-service data is data used to describe process use case data of corresponding businesses. The process use case types of multiple businesses include office management, supply chain management, and customer service management of an enterprise or organization.
[0057] Specifically, the preset database stores business process use case data of multiple enterprises or organizations, which can serve as service providers of the cloud platform, that is, tenants of the cloud platform. The cloud platform is generally managed by a cloud server, so the preset database can be a cloud database deployed in a cloud platform environment managed by a cloud server, or a data center at the enterprise or organization end. In addition, the process use case data of each business can be described by one or more meta-service data.
[0058] For example, for enterprises in the financial industry, the types of process use cases of their business may include: loan approval, risk assessment and insurance claims. For enterprises in the manufacturing industry, the types of process use cases of their business may include: production order management, quality control and equipment maintenance, etc. Among them, for the process use case data of loan approval, the meta-service data used to describe it may be Loan_ID (loan business identification), Credit_History (historical credit), Loan_Status (loan approval status) and LoanAmount (applicant income).
[0059] Step 202: Preprocessing is performed on parameters corresponding to each meta-service data according to the set process arrangement requirements. The preprocessing operation includes editing, converting and obtaining values of the parameters.
[0060] The process arrangement requirement may be determined manually, and the purpose of performing the preprocessing operation on each meta-service data is to convert each meta-service data into a unified unit or format to facilitate subsequent JSON standardization processing.
[0061] For example, the corresponding parameter of the above-mentioned LoanAmount is usually already a numeric type, but a preprocessing operation needs to be performed to ensure that the value of the parameter is expressed in a uniform unit (such as thousands). Or, for the above-mentioned Credit_History and Loan_Status, a preprocessing operation needs to be performed to convert them into numeric data, for example, 1 for compliance with guidelines or approval, and 0 for non-compliance or failure to be approved. Finally, for the above-mentioned Loan_ID, a preprocessing operation needs to be performed to convert it into the corresponding parameters of one or more other meta-service data, for example, it can be converted to Gender=1 (indicating gender, that is, male=1, female=0).
[0062] Step 203: Perform JSON standardization on the pre-processed meta-service data.
[0063] The purpose of JSON standardization is to facilitate the subsequent reading and processing of each meta-service data by MongoDB.
[0064] For example, in the JSON structure obtained after JSON standardization, there are LoanAmount: 5000 (indicating that the applicant's income has been assigned a value), Credit_History: 1, Loan_Status: 0 (indicating that the applicant meets the guidelines but has not been approved), and Gender: 1 (indicating that the borrower is male). In this way, the categorical variables (Credit_History, Loan_Status) in each meta-service data after preprocessing are assigned values, missing values are filled (LoanAmount: 5000), and abnormal values (Loan_ID) are processed into available variables (Gender: 1) to meet the requirements of subsequent MongoDB model input.
[0065] Step 204: Save each meta-service data after JSON standardization processing into a preset distributed document storage database MongoDB.
[0066] Among them, for different types of process use case data, the cloud server can create different types of databases and their collections in MongoDB to implement the storage of meta-service data.
[0067] For example, the cloud server can first connect to the locally running MongoDB instance, and then select a database named LoanDatabase and a collection named LoanApplications (which is set for the above loan approval process use case data). Then define a string containing the above JSON structure and use json.loads to parse it into a Python object. Finally, the cloud server uses the insert_many method to batch insert the various meta-service data after JSON standardization into the collection LoanApplications.
[0068] Step 205: Based on the meta-service data in MongoDB, a dynamic process model is constructed using the preset Activiti tool.
[0069] Specifically, the cloud server first obtains each meta-service data from different collections in MongoDB, and then can use the Activiti tool to deploy a single dynamic process definition, and call each meta-service data in turn to start a single dynamic process instance. Among them, the dynamic process instance will dynamically adjust the execution of the process instance according to the currently called meta-service data to complete the construction of the above dynamic process model.
[0070] For example, the cloud server first obtains the loan approval data (including LoanAmount: 5000, Credit_History: 1, Loan_Status: 0, Gender: 1) from the LoanApplications collection, then deploys a process definition and starts a process instance based on the loan approval data. This process instance will then dynamically adjust the execution of the process instance based on the data subsequently called from different collections in MongoDB.
[0071] Step 206: Orchestrate each meta-service data in the dynamic process model to obtain a process orchestration result.
[0072] The orchestration of each meta-service data is determined by the corresponding system process or application requirements. In addition, the corresponding process definition should be deployed in Activiti for each business meta-service data to start the corresponding process use case, and each business has a clear execution logic.
[0073] For example, assuming that a process definition named dynamicLoanApprovalProcess has been deployed in Activiti, the cloud server first obtains the loan approval data from the collection of LoanApplications, then starts a process use case named dynamicLoanApprovalProcess and passes the loan approval data as a variable. Next, the cloud server executes the task process use case and obtains the final output value of the variable, and finally checks whether the process is finished. In this way, the process use cases corresponding to the meta-service data of all businesses are executed in sequence, and multiple final output values are obtained to obtain the process orchestration result.
[0074] In some embodiments, each meta-service data in the dynamic process model is extracted from the dynamic process model using a preset warehouse technology ETL tool; and each meta-service data in the dynamic process model is arranged, including:
[0075] According to the predefined data mapping relationship and conversion rules, the format or structure conversion arrangement is performed on each meta-service data in the dynamic process model.
[0076] Among them, the data mapping relationship and conversion rules are determined according to the process orchestration requirements. ETL tools provide significant efficiency improvements and quality assurance in the process of data extraction, conversion and orchestration. They automate data processes, reduce manual errors, and ensure data consistency and accuracy. ETL tools can handle large amounts of data and support multiple data sources and formats, making data integration simple and fast. In addition, they also provide data cleaning and conversion functions to help improve data quality and provide a reliable foundation for analysis and decision-making. Through the orchestration capabilities of ETL tools, complex data workflows can be created to achieve timed data extraction and processing, thereby supporting real-time data analysis and reporting.
[0077] For example, loan approval data including LoanAmount: 5000, Credit_History: 1, Loan_Status: 0, and Gender: 1 can be converted and organized into the following structure: medium income → good → not approved → male. Therefore, using ETL tools, data can be cleaned and converted to meet the needs of different systems or applications and ensure data accuracy and consistency.
[0078] In the above process orchestration method, by combining MongoDB storage with the Activiti tool to build a dynamic process model, it not only improves the efficiency of data processing, but also expands its application scope, making process management more flexible and responsive to business changes. It can automatically perform process orchestration in the face of multi-tenants, multiple environments and complex scenarios, thereby improving process execution efficiency and performance.
[0079] In some embodiments, the preset database is a relational database management system MySQL or MongoDB deployed in a multi-tenant cloud platform environment, and the process orchestration result includes a plurality of process use case data that have completed meta-service data format or structure conversion orchestration; after obtaining the process orchestration result, such as Figure 3 , 4 As shown, including:
[0080] Step 301: In response to a target business trigger instruction from a user, a first process use case corresponding to the target business is executed.
[0081] The target business trigger instruction can be sent from the user end to the cloud server, so that the cloud server executes the target business specified by the target business trigger instruction, and the target business can correspond to one or more first process use cases. For example, when the target business is risk assessment, the corresponding first process use cases can be credit risk assessment, investment risk assessment, and savings risk assessment.
[0082] Step 302: Obtain first process use case data corresponding to the first process use case from the cloud platform environment, and convert the first process use case data into a business process modeling notation BPMN XML standard format.
[0083] The first process use case is visualized in the BPMN modeling tool and can be executed by the BPMN engine belonging to the Activiti tool, and each element corresponding to a different first process use case is given a unique ID and name for easy identification and processing. In addition, each first process use case contains a corresponding start event, user task, and end event.
[0084] For example, for the above-mentioned credit risk assessment, investment risk assessment and savings risk assessment, the cloud server needs to define three independent process use cases in the BPMN XML document, and assign corresponding unique IDs "CreditRiskAssessment, InvestmentRiskAssessment, SavingsRiskAssessment" and corresponding names "<!--Credit Risk Assessment-->, <!--Investment Risk Assessment-->, <!--Savings Risk Assessment-->" to them according to actual business needs. At the same time, they also need to be assigned corresponding start events, user tasks and end events to complete the conversion of the first process use case data to the BPMN XML standard format. Among them, for investment risk assessment, the corresponding start events, user tasks and end events can be: "StartEvent_Investment" name=, UserTask_Investment" name=, EndEvent_Investment" name=".
[0085] Step 303: Based on the first process use case data converted into the BPMN XML standard format, create a business orchestration engine SmartEngine to execute the target business logic of the first process use case, obtain the process use case processing result of the first process use case and save it to a preset database.
[0086] Specifically, before the cloud server creates SmartEngine, it needs to ensure that it is equipped with an XML file that complies with the BPMN 2.0 standard. Then the cloud server creates a configuration class to initialize SmartEngine and deploys the BPMN XML file. Finally, the SmartEngine is used to start the process use case and execute the business logic. For example, the processing result of the process use case for investment risk assessment can be Risk_Investment"name=3 (high risk), and then the cloud server saves the Risk_Investment"name= to MySQL or MongoDB.
[0087] In the above-mentioned process orchestration method, by first converting the first process use case data into a BPMN XML standard format document and then starting SmartEngine to execute the corresponding business logic, it can promote cross-system data transmission and parsing, ensure accurate transmission and understanding of information, and improve process execution efficiency and performance in the face of multi-tenants, multiple environments and complex scenarios.
[0088] In some embodiments, a business orchestration engine SmartEngine is created to execute the target business logic of the first process use case, such as Figure 4 , 5 As shown, including:
[0089] Step 501: Parse the first process use case data converted into the BPMN XML standard format into an internal use case model of SmartEngine, and define context data for the internal use case model;
[0090] The internal use case model is a use case model inherent in SmartEngine. When the cloud server parses the first process use case data into the internal use case model, it is generally necessary to convert the BPMN XML standard format file into a model object of SmartEngine. In addition, the context data usually includes process variables and business data, which are used during the execution of the process use case.
[0091] Step 502: Based on the internal use case model in which context data has been defined, a new second process use case is created through the application programming interface API of SmartEngine.
[0092] Specifically, the second process use case is the same one or more use cases created based on the first process use case.
[0093] For example, if it is known that the first process use cases are credit risk assessment, investment risk assessment and savings risk assessment, then the internal use case model of multiple second process use cases and context data should include the relevant logic and data structure of credit risk assessment, investment risk assessment and savings risk assessment.
[0094] Step 503: Start the second process use case by sending a start event to the second process use case, and execute the corresponding service node processing, gateway node logic determination and event node timing process.
[0095] For example, for the second process use case of investment risk assessment, after the cloud server sends StartEvent_Investment"name=(start event) to the investment risk assessment, it starts the investment risk assessment to execute the process of UserTask_Investment"name=(corresponding service node processing), Decision_Investment"name=(corresponding gateway node logical decision) and Timed event_Investment"name=(corresponding event node timing).
[0096] Step 504: The process case processing result of the second process case is used as the process case processing result of the first process case and is saved in a preset database.
[0097] For example, the process case processing result for investment risk assessment may be Risk_Investment"name=3 (high risk), and then the cloud server uses Risk_Investment"name=3 as the process case processing result of the first process case and saves it to MySQL or MongoDB.
[0098] In the above-mentioned process orchestration method, since SmartEngine is started to execute the corresponding business logic, it can promote cross-system data transmission and analysis, ensure the accurate transmission and understanding of information, and improve the efficiency and performance of process execution when facing multi-tenants, multiple environments and complex scenarios.
[0099] In some embodiments, when executing the corresponding service node processing, gateway node logic determination and event node timing process, the method further includes:
[0100] If any of the processes of service node processing, gateway node logic determination, and event node timing is abnormal, the abnormal data and its context-related data will be saved in a preset database.
[0101] For example, for the second process use case of investment risk assessment, assuming that an exception occurs when starting the investment risk assessment to execute UserTask_Investment"name=", the cloud server saves the exception UserTask_Investment"name=" to MySQL or MongoDB.
[0102] In this way, accurate transmission and understanding of information can be ensured, and the storage of abnormal data can be avoided, so as to improve the performance of process execution when facing multi-tenants, multiple environments and complex scenarios.
[0103] In some embodiments, Figure 4As shown, the process of service node processing is executed by the HSF executor called from the cloud platform environment. The application context environment appContext in the HSF executor also includes the pre-processor and the post-processor;
[0104] The pre-processor is used to splice the parameters input to the service node, verify the validity of the parameters, prepare the environment required for the service node processing, and send a processing request to the post-processor;
[0105] The postprocessor starts running after the service node is processed to verify whether the output result of the service node processing meets expectations; if the output result meets expectations, the output result is saved to the database; or, if the output result is abnormal and does not meet expectations, a rollback operation is performed on the abnormal output result.
[0106] The expected output result is an interval determined according to the empirical range of the output result of the corresponding service node.
[0107] For example, for the second process use case of investment risk assessment, the preprocessor can be used to splice the parameters UserTask_Investment"name=1, 2, 3 input to the service node, verify the validity of these parameters, prepare the environment http / hsf UserTask_Investment required for service node processing, and send a processing request to the postprocessor.
[0108] Then, if it is determined that these parameters are valid, the post-processor is used to verify whether the output result Risk_Investment"name=9 processed by the service node meets expectations; if the output result meets expectations Risk_Investment"name<=10, the output result is saved in the database; or, if the output result is abnormal and does not meet expectations Risk_Investment"name<=5, a rollback operation is performed on the abnormal output result.
[0109] In this way, we can further ensure the accurate transmission and understanding of information, avoid the storage of abnormal data, and improve the process execution performance when facing multi-tenants, multiple environments and complex scenarios.
[0110] In some embodiments, the Activiti tool is installed with an Activiti Modeler tool; based on the various meta-service data in MongoDB, a dynamic process model is constructed using the preset Activiti tool, such as Figure 6 As shown, including:
[0111] Step 601: Use the Activiti Modeler tool to design an initial flow chart.
[0112] For example, for companies in the financial industry, the types of nodes that need to be reserved in the initial flowchart include loan approval, risk assessment, and insurance claims.
[0113] Step 602: define multiple nodes in the initial flow chart according to the business requirements corresponding to the multiple businesses, each node being one of a start event, a user task and a gateway.
[0114] Then, the cloud server may define the node whose node type in the initial flow chart is investment risk assessment as StartEvent_Investment"name=(start event).
[0115] Step 603: setting a flow path between each node according to the relationship between each meta-service data, and setting the connection order and conditions for each node;
[0116] Next, the cloud server can set the order and conditions of connecting the node StartEvent_Investment"name="with UserTask_Investment"name="and EndEvent_Investment"name=".
[0117] Step 604: define the variables required for each decision point for the current initial flow chart according to the decision points in each meta-service data;
[0118] The decision point refers to a node with a corresponding threshold variable set. For example, the variable required for another decision point of the above-mentioned investment risk assessment node can be defined as Risk_Investment"name<=3, that is, when the investment risk score does not exceed 3, the next task flow is executed.
[0119] Step 605: Based on the process branches and exclusive gateways existing in each meta-service data, corresponding conditional expressions are set for the current initial flow chart to construct a dynamic process model.
[0120] Among them, the conditional expression of the setting is used to set the order and conditions of the connection of each node plus the corresponding process branch and exclusive gateway. For example, the conditional expression of the process branch of the node of the above-mentioned investment risk assessment can be if History_Investment"name<=3, that is, when the historical investment risk score does not exceed 3, execute another step of the task process. And the corresponding exclusive gateway can be set to Decision_Investment"name=4, that is, except for the use case process with investment risk ID 4, which is available at this node, the processes of other use cases are not available at this node.
[0121] In this way, we can further ensure the accurate transmission and understanding of information, ensure the correctness and executability of the process, and improve the process execution performance when facing multi-tenants, multiple environments and complex scenarios.
[0122] It should be noted that, with respect to the various steps included in the metadata-based process orchestration method provided in any of the above embodiments, unless otherwise clearly stated in this document, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of these steps can include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.
[0123] In another embodiment of the present application, the metadata-based process orchestration method can be applied to Figure 1 The cloud server 102 shown in FIG. Figure 7 In the meta-service business architecture shown, the user terminal 101 can first trigger the data scheduling function in the basic service layer to instruct the cloud server 102 to send data from the data center, that is, Figure 1 The meta-service data of multiple businesses are obtained from the preset database 103 in the cloud server 102. Then the user end 101 can trigger the tool orchestration function to enable the cloud server 102 to perform preprocessing on the corresponding parameters of each meta-service data using general tools in the business layer according to the set process orchestration requirements, and perform JSON standardization on each meta-service data after preprocessing. Next, the cloud server 102 performs the use case management function in the business layer, and saves each meta-service data after JSON standardization to the MongoDB in the data storage layer. Based on each meta-service data in MongoDB, the process orchestration function is triggered in the business layer, and a dynamic process model is constructed using the preset Activiti tool. Finally, each meta-service data in the dynamic process model is orchestrated to obtain a process orchestration result. Among them, the preset database 103 stores the business process use case data of multiple enterprises or organizations. These enterprises or organizations can serve as service providers of the cloud platform, so the preset database 103 has been on the cloud. The preset database 103 can be MongoDB or MySQL deployed in the cloud platform environment managed by the cloud server, or it can be a data center on the enterprise or organization side. Through the steps of the above method, it is possible to automatically orchestrate processes in the face of multi-tenants, multiple environments and complex scenarios, thereby improving process execution efficiency and performance.
[0124] According to a second aspect, the present application provides a process arrangement device based on metadata, such as Figure 8As shown, the process arrangement device includes:
[0125] The meta-service data acquisition module 110 is used to acquire meta-service data of multiple services from a preset database, where the meta-service data is data used to describe process use case data of the corresponding services, where the process use case types of the multiple services include office management, supply chain management, and customer service management of an enterprise or organization;
[0126] The parameter preprocessing module 120 is used to perform preprocessing on the parameters corresponding to each meta-service data according to the set process arrangement requirements. The preprocessing operation includes editing, converting and obtaining values of the parameters.
[0127] The JSON standardization module 130 is used to perform JSON standardization on each pre-processed meta-service data;
[0128] The metadata storage module 140 is used to store each meta-service data after JSON standardization processing in a preset distributed document storage database MongoDB;
[0129] The process model building module 150 is used to build a dynamic process model based on various meta-service data in MongoDB using a preset Activiti tool;
[0130] The dynamic process orchestration module 160 is used to orchestrate each meta-service data in the dynamic process model to obtain a process orchestration result; wherein the orchestration method corresponding to each meta-service data is determined by its corresponding system process or application requirement.
[0131] In some embodiments, the process orchestration device also includes a process execution module 170, the preset database is a relational database management system MySQL or MongoDB deployed in a multi-tenant cloud platform environment, and the process orchestration result includes multiple process use case data that have completed meta-service data format or structure conversion orchestration; after obtaining the process orchestration result, the process execution module 170 is used to respond to a target business trigger instruction from a user, execute a first process use case corresponding to the target business, obtain first process use case data corresponding to the first process use case from the cloud platform environment, convert the first process use case data into a business process modeling notation BPMN XML standard format, create a business orchestration engine SmartEngine based on the first process use case data converted into the BPMN XML standard format to execute the target business logic of the first process use case, obtain the process use case processing result of the first process use case and save it to the preset database.
[0132] In some embodiments, the process execution module 170 is also used to parse the first process use case data that has been converted into the BPMN XML standard format into an internal use case model of the SmartEngine, and define context data for the internal use case model; based on the internal use case model with defined context data, create a new second process use case through the SmartEngine application programming interface API; start the second process use case by sending a start event to the second process use case, and execute the corresponding service node processing, gateway node logic judgment and event node timing process; use the process use case processing result of the second process use case as the process use case processing result of the first process use case and save it in a preset database.
[0133] In some embodiments, when executing the corresponding service node processing, gateway node logic determination and event node timing processes, the process execution module 170 is also used to save the exception data and its context-related data to a preset database if an exception occurs in any of the processes including service node processing, gateway node logic determination and event node timing.
[0134] In some embodiments, the Activiti tool is installed with the Activiti Modeler tool; the process model construction module 150 is also used to design an initial flowchart using the Activiti Modeler tool; multiple nodes are defined in the initial flowchart according to the business needs corresponding to multiple businesses, and each node is one of the start events, user tasks and gateways; a flow path is set between each node according to the relationship between each meta-service data, and the connection sequence and conditions are set for each node; the variables required for each decision point in the current initial flowchart are defined according to the decision points in each meta-service data; based on the process branches and exclusive gateways existing in each meta-service data, corresponding conditional expressions are set for the current initial flowchart to build a dynamic process model.
[0135] In some embodiments, each meta-service data in the dynamic process model is extracted from the dynamic process model using a set warehouse technology ETL tool; the dynamic process orchestration module 160 is also used to perform format or structure conversion orchestration on each meta-service data in the dynamic process model according to predefined data mapping relationships and conversion rules.
[0136] For specific limitations applicable to metadata-based process orchestration devices, please refer to the limitations applicable to metadata-based process orchestration methods above, which will not be repeated here. Each module in the above-mentioned metadata-based process orchestration device can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0137] According to a third aspect, the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the metadata-based process orchestration methods in the above-mentioned embodiments are implemented.
[0138] According to a fourth aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes, the steps of any one of the metadata-based process orchestration methods in the above-mentioned embodiments are implemented.
[0139] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to process orchestration. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, any of the above-mentioned metadata-based process orchestration methods is implemented.
[0140] Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (RamCUs), direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0141] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0142] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.
[0143] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
Claims
1. A metadata-based process orchestration method, characterized in that: The method comprises: Obtaining meta-service data of multiple businesses from a preset database, wherein the meta-service data is data used to describe process use case data of corresponding businesses, and the process use case types of the multiple businesses include office management, supply chain management, and customer service management of an enterprise or organization; According to the set process arrangement requirements, preprocessing is performed on the parameters corresponding to each of the meta-service data, and the preprocessing operation includes editing, converting and obtaining values of the parameters; Performing JSON standardization on each of the preprocessed meta-service data; The meta-service data after JSON standardization is saved in a preset distributed document storage database; Based on each of the meta-service data in the distributed document storage database, a dynamic process model is constructed using a preset process modeling tool; Each of the meta-service data in the dynamic process model is orchestrated to obtain a process orchestration result; wherein the orchestration method corresponding to each of the meta-service data is determined by the corresponding system process or application requirement.
2. The method according to claim 1, characterized in that The preset database is a relational database management system or the distributed document storage database deployed in a multi-tenant cloud platform environment, and the process orchestration result includes a plurality of process use case data that have completed meta-service data format or structure conversion orchestration; After obtaining the process arrangement result, the method further includes: In response to a target business trigger instruction from a user, executing a first process use case corresponding to the target business; Acquire first process use case data corresponding to the first process use case from the cloud platform environment, and convert the first process use case data into a business process modeling notation BPMN XML standard format; According to the first process use case data converted into the BPMN XML standard format, a business orchestration engine is created to execute the target business logic of the first process use case, obtain the process use case processing result of the first process use case and save it to the preset database.
3. The method according to claim 2, characterized in that The creating of a business orchestration engine to execute the target business logic of the first process use case includes: Parsing the first process use case data converted into the BPMN XML standard format into an internal use case model of the business orchestration engine, and defining context data for the internal use case model; Based on the internal use case model in which context data has been defined, creating a new second process use case through the application programming interface of the business orchestration engine; The second process use case is started by sending a start event to the second process use case, and the corresponding service node processing, gateway node logic determination and event node timing process are executed; The process use case processing result of the second process use case is used as the process use case processing result of the first process use case and is saved in the preset database.
4. The method according to claim 3, characterized in that When executing the corresponding service node processing, gateway node logic determination and event node timing process, the method further includes: If any of the processes of the service node processing, gateway node logic determination and event node timing is abnormal, the abnormal data and its context-related data are saved in the preset database.
5. The method according to claim 3, characterized in that: The process of the service node processing is executed by the HSF executor called from the cloud platform environment, and the application context environment in the HSF executor also includes a pre-processor and a post-processor; The pre-processor is used to splice the parameters input to the service node, verify the validity of the parameters, prepare the environment required for the service node processing, and send a processing request to the post-processor; The preprocessor starts running after the service node processes, and is used to verify whether the output result of the service node processing meets expectations; if the output result meets expectations, the output result is saved in the database, or, if the output result is abnormal and does not meet expectations, a rollback operation is performed on the abnormal output result.
6. The method according to claim 2, characterized in that The Activiti tool is installed with an ActivitiModeler tool; the dynamic process model is constructed based on each of the meta-service data in the distributed document storage database using the preset Activiti tool, including: Design an initial flow chart using the ActivitiModeler tool; Defining a plurality of nodes in the initial flow chart according to the business requirements corresponding to the plurality of businesses, each of the nodes being one of a start event, a user task and a gateway; Setting a flow path between each of the nodes according to the relationship between each of the meta-service data, and setting a connection sequence and condition for each of the nodes; Defining variables required for each decision point for the current initial flow chart according to each decision point in the meta-service data; Based on the process branches and exclusive gateways existing in each of the meta-service data, corresponding conditional expressions are set for the current initial flow chart to construct the dynamic process model.
7. The method according to claim 1, characterized in that Each of the meta-service data in the dynamic process model is extracted from the dynamic process model using a set warehouse technology ETL tool; The arranging each meta-service data in the dynamic process model includes: According to the predefined data mapping relationship and conversion rules, format or structure conversion arrangement is performed on each of the meta-service data in the dynamic process model.
8. A process scheduling device based on metadata, characterized in that: The process arrangement device comprises: A meta-service data acquisition module is used to acquire meta-service data of multiple services from a preset database, wherein the meta-service data is data used to describe process use case data of corresponding services, and the process use case types of the multiple services include office management, supply chain management, and customer service management of an enterprise or organization; A parameter preprocessing module, used to perform preprocessing on the parameters corresponding to each of the meta-service data according to the set process arrangement requirements, wherein the preprocessing operation includes editing, converting and obtaining values of the parameters; A JSON standardization module, used for performing JSON standardization on each of the pre-processed meta-service data; A metadata storage module, used to save each of the meta-service data after JSON standardization processing into a preset distributed document storage database; A process model building module, used to build a dynamic process model based on each of the meta-service data in the distributed document storage database using a preset Activiti tool; The dynamic process orchestration module is used to orchestrate each of the meta-service data in the dynamic process model to obtain a process orchestration result; wherein the orchestration method corresponding to each of the meta-service data is determined by its corresponding system process or application requirement.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.