WO2023040690A1 - Data processing method, program product, readable medium, and electronic device - Google Patents

Abstract

The present application relates to a data processing method, a program product, a readable medium, and an electronic device. The method is based on an Activiti service flow engine and a search engine, the Activiti service flow engine uses a database, and the database comprises related data of a service process. The method comprises: on the basis of a service requirement, at least obtaining first target data and second target data from a database of an Activiti service flow engine; at least combining the first target data and the second target data so as to generate a data structure, the data structure comprising a relationship description file of the first target data and the second target data; and writing the data structure into a search engine for the Activiti service flow engine to obtain the data structure by accessing the search engine. By storing data that is in a database in the search engine, the Activiti service flow engine obtains relevant data of a partial service process from the search engine by accessing the search engine, thus reducing the frequency of the Activiti service flow engine accessing the database, and improving the performance of an application system of the Activiti service flow engine.

Description

A data processing method, program product, readable medium and electronic device

This application claims the priority of the Chinese patent application submitted to the State Intellectual Property Office on September 17, 2021, with the application number 202111092873.6 and the application name "A data processing method, program product, readable medium and electronic device". The entire contents are incorporated by reference in this application.

technical field

The present application relates to the technical field of software, and in particular to a data processing method, program product, readable medium and electronic equipment.

Background technique

Activiti is a lightweight, embeddable business process engine for scalable cloud architectures. Activiti covers business process management, workflow (Workflow), service collaboration and other fields. It is an open source, flexible, and easily extensible executable process language framework that supports the new BPMN 2.0 (BPMN-Business Process Modeling Notation business process Modeling notation) standard.

Workflow is the automation of part or the whole of a business process in a computer application environment. It is an abstract and general description of the business rules between the workflow and its various operation steps, that is, the logic and rules that organize the work in the workflow together before and after. Represent it in a computer with an appropriate model and perform calculations on it. The main problem to be solved by workflow is: in order to achieve a certain business goal, among multiple participants, use computers to automatically transfer documents, information or tasks according to certain predetermined rules. To achieve a desired business objective in this way, or to contribute to the achievement of that objective.

The Acitiviti business flow engine stores the relevant data of the business process, including the process definition of the workflow, the flow control when the process is running, and the data and documents transmitted in the process, etc. in the database.

Therefore, the Activiti business flow engine will frequently access the database when doing business flow, which not only causes a large processing load on the database, but also limits the performance of the entire application system including the Acitiviti business flow engine. To build a database environment that supports large amounts of data and high throughput requires high computer hardware costs and database software license fees.

Contents of the invention

Some embodiments of the present application provide a data processing method, a program product, a readable medium, and an electronic device. The following describes the present application from various aspects, and the implementation manners and beneficial effects of the following various aspects can be referred to each other.

In the first aspect, the implementation of the present application provides a data processing method, the method is based on the Activiti business flow engine and the search engine, the Activiti business flow engine uses a database, and the database contains relevant data of the business process, the method includes: Activiti based on business requirements The business flow engine obtains at least the first target data and the second target data from the database; at least combines the first target data and the second target data to generate a data structure, wherein the data structure includes the first target data and the second target data The relational description file of the target data; write the data structure into the search engine, so that the Activiti business flow engine can obtain the data structure by accessing the search engine.

In this embodiment, by storing the data in the database in the search engine, the Activiti business flow engine accesses the search engine, uses the search engine to search for relevant data of some business processes, obtains relevant data of some business processes from the search engine, and reduces the Activiti business The frequency that the flow engine accesses the database improves the performance of the application system of the Acitiviti business flow engine.

In an implementation of the above-mentioned first aspect, the above-mentioned data structure is written into the search engine for the Activiti business flow engine to obtain the data structure by accessing the search engine, including: optimizing the data structure based on the search engine to obtain the target The data structure, wherein the text of the target data structure supports the search conditions of the search engine; write the target data structure into the search engine, so that the Activiti business flow engine can obtain the target data structure by accessing the search engine.

In an implementation of the above first aspect, the above text includes data type and/or format.

In an implementation of the above-mentioned first aspect, the above-mentioned search engine is a distributed search engine; the Activiti business flow engine integrates a distributed transaction framework to realize that the Activiti business flow engine and the search engine are at least about the first target data and the second target Data consistency and integrity.

In an implementation of the above-mentioned first aspect, the above-mentioned distributed transaction framework is one of Seata (Simple Extensible Autonomous Transaction Architecture), LCN (Local Communication Network), and EasyTransaction.

In an implementation of the first aspect above, the first target data and the second target data are acquired based on the number of visits or data volume of the Activiti business flow engine.

In an implementation of the above-mentioned first aspect, the above-mentioned search engine is Elasticsearch or Solr.

In an implementation of the first aspect above, the first target data and the second target data include at least one of business-related task data, business-related business data, and business-related process data delivery.

In an implementation of the above-mentioned first aspect, the above-mentioned business includes at least one of insurance business-related preliminary examination of orders, new order registration, entry review, underwriting, registration and creation of insurance policies, and policy delivery.

In a second aspect, the embodiments of the present application provide a computer program product, where the computer program product includes instructions, and the instructions are used to implement the above-mentioned data processing method.

In a third aspect, the embodiments of the present application provide a readable medium, on which instructions are stored, and when the instructions are executed on the electronic equipment, the electronic equipment executes the data processing method as described above.

In a fourth aspect, an embodiment of the present application provides an electronic device, which includes: a memory for storing instructions executed by one or more processors of the electronic device, and a processor that is the One of the processors of the device is used to execute the data processing method as described above.

Description of drawings

Fig. 1 is the scene diagram that a kind of Activiti business flow engine 100 obtains data according to some embodiments of the present application;

Fig. 2 is a kind of flow chart of the data processing method based on Activiti business flow engine 100 and search engine 300 according to some embodiments of the present application;

Fig. 3 is a schematic diagram of data processing according to some embodiments of the present application;

Fig. 4 is a schematic diagram of data processing of a new order registration task according to some embodiments of the present application;

Fig. 5 is a schematic diagram of a data structure according to some embodiments of the present application;

Fig. 6 is the flow chart of another kind of data processing method based on Activiti business flow engine 100 and search engine 300 according to some embodiments of the present application;

Fig. 7 is a block diagram of an electronic device according to some embodiments of the present application.

Detailed ways

The illustrative embodiment of the present application includes but not limited to a kind of data processing method, device, readable medium and electronic equipment based on Activiti service flow engine and search engine 300.

Fig. 1 is a scenario diagram of data access by an Activiti service flow engine 100 according to some embodiments of the present application.

As shown in Figure 1, in some embodiments, the relevant data of the business process of Activiti business flow engine 100 is stored in the database 200 of Activiti; During the operation of Activiti business flow engine 100, it is necessary to frequently access the database 200 to obtain the business process The related data, especially the frequent acquisition of the above-mentioned workflow-related data, imposes a large processing load on the database 200, which in turn leads to limited performance of the application system including Acitiviti.

Based on the above reasons, this application provides a data processing method based on Activiti business flow engine 100 and search engine 300. By storing the data in database 200 in search engine 300, Activiti business flow engine 100 accesses search engine 300 and uses search The engine 300 searches the relevant data of part of the business process, obtains the relevant data of the part of the business process from the search engine 300, thereby reducing the burden of the database 200, and improving the performance of the application system of Acitiviti.

Fig. 2 is a flow chart of a data processing method based on Activiti business flow engine 100 and search engine 300 according to some embodiments of the present application; Fig. 3 is a kind of data processing method based on Activiti business flow engine 100 and search engine 300 according to some embodiments of the present application A schematic diagram of data processing by the engine 300; FIG. 4 is a schematic diagram of data processing of a new order registration task according to some embodiments of the present application; FIG. 5 is a schematic diagram of a data structure according to some embodiments of the present application. The above data processing method will be described in detail below with reference to FIG. 2 to FIG. 5 .

In some embodiments of the present application, the Activiti business flow engine 100 includes a database 200, and the database 200 contains relevant data of the business process. The data processing method provided by this application is shown in Figure 2, and includes the following steps.

402: Acquire first target data and second target data. Specifically, at least the first target data and the second target data are acquired from the database 200 based on business requirements.

In some embodiments of the present application, as shown in FIG. 3 , the database 200 includes definition data 110 and instance data 120, and the definition data 110 includes definitions of variables involved in related businesses; the instance data 120 includes process flow control data 121 and The task-related data 122 part, the task-related data 122 includes some or all task-related data of each task involved in the related business, wherein the task-related data includes task data (i.e., example data of task execution), business data (i.e., task execution data). variable data) and process data (that is, the process flow data of task execution).

Taking the new order registration task in the insurance business process as an example, as shown in FIG. 3 , all relevant data of the new order registration are stored in the database 200 . Among them, the definition data 110 and the instance data 120 respectively include the definition data and the instance data of the new order registration, and the instance data 120 stores some or all task-related data of the new order registration, including: new order registration-related task data, new order registration-related business Data and process data related to new order registration. New order registration related task data includes: process identification, process name, task identification, task name, process instance identification (not shown in the figure), task instance identification (not shown in the figure), task status (not shown in the figure ), task executor (not shown in the figure), task creation date (not shown in the figure), completion date (not shown in the figure), etc. The new policy registration-related business data stored in the instance data 120 includes: policy identification, inquiry number, policyholder, insured, agent, policy starting and ending dates, and so on. The process data related to the new order registration is copied to the process flow data related to the new order registration in the process flow control data 121 .

404: Merge the first target data and the second target data to generate the data structure 400. Specifically, at least the first target data and the second target data are combined to generate a data structure, wherein the data structure includes a relationship description file between the first target data and the second target data.

In some embodiments of the present application, as shown in FIG. 3 , a data structure 400 is generated based on the task data, business data and process data of a specific task in the task-related data 122 . For example, as shown in FIG. 4 , the data structure 400 is generated based on the task data, business data and process data of the new order registration task in the task-related data 122 .

In some embodiments of the present application, as shown in FIG. 5 , the data structure 400 includes task data related to new order registration (hereinafter referred to as “new order registration related”), business data related to new order registration, and new order registration-related To register related process data, the data structure 400 includes a description of the relationship between task data, a description of the relationship between business data, and a description of the relationship between process data. Wherein, the relationship description includes relationship descriptions such as correspondence relationship, sequence relationship, and reference relationship.

For example, the data structure 400 shown in FIG. 5 includes descriptions for describing the correspondence, sequence, reference, etc. between process identifiers, task identifiers, process names and task names. For example, the relationship description includes the following content: the process name of the new order registration task is "inquiry", and the process identifier corresponding to "inquiry" is "L1"; the task name of "inquiry" includes "entering order number information" and " "Calculate cost", "Enter bill number information" and "Calculate cost" correspond to task identifiers "R1" and "R2" respectively; and "Enter bill number information" first, then "Calculate cost".

In some embodiments of the present application, the logical structure of the data structure body 400 is at least one of a set, a linear structure, a tree structure, and a graph structure, and the relationship between the data corresponding to each logical structure is respectively the same set, One-to-one relationships, one-to-many relationships, and many-to-many relationships.

406 : Write the data structure 400 into the search engine 300 . Specifically, the data structure 400 is written into the search engine 300 so that the Activiti service flow engine 100 can obtain the data structure 400 by accessing the search engine 300 .

In the above-mentioned method shown in FIG. 2 and related embodiments, by saving part of the data of the Activiti business flow engine 100 into the search engine 300, the speed at which the Activiti business flow engine 100 acquires data is improved, and multiple data are merged into a data structure 400 , reducing the frequency of reading and writing data by the Activiti business flow engine 100 . For example, based on an instruction of the Activiti business flow engine 100 to access the first target data, the search engine 300 will search the data structure 400 for the first target data and the second target data related to the first target data, and the first Both the target data and the second target data are sent to the Activiti business flow engine 100, and the second target data is the data that the Activiti business flow engine 100 needs to access later, so the number of visits to the second target data is reduced, thereby reducing The reading and writing frequency of the Activiti business flow engine 100 .

FIG. 6 is a flow chart of another data processing method based on the Activiti business flow engine and the search engine 300 according to some embodiments of the present application, wherein steps 402 and 404 are the same as the above steps, and will not be repeated here.

In some embodiments of the present application, the above step 406: write the data structure 400 into the search engine 300, including as shown in FIG. 6:

4061: Based on the search conditions of the search engine 300 and the text of the data structure 400, obtain the target data structure 400. Specifically, based on the search engine 300 and the data structure 400 , the target data structure 500 is obtained, wherein the text of the target data structure 500 supports the search condition of the search engine 300 .

4062: Write the target data structure 500 into the search engine 300. Specifically, the target data structure 500 is written into the search engine 300 so that the Activiti business flow engine 100 can obtain the target data structure 500 by accessing the search engine 300 .

In this embodiment, based on the text supported by the search engine 300, the text of the data structure 400 is optimized to obtain the target data structure 500 that supports the search conditions of the search engine 300, so as to save the speed of the search engine 300 for searching data.

In some embodiments of the present application, the above text includes data types and/or formats.

In some embodiments of the present application, the task data and process data will not change during the use of the Activiti business flow engine 100. When the first target data and the second target data are respectively one of task data and process data, The above optimization includes presetting the data type of the first target data or the second target data. When the data structure 400 is written into the search engine 300 , the data types of the first target data and the second target data are automatically set according to the preset data types, and the target data structure 500 is obtained. For example, the first target data is a text field, the second target data is a numeric field, and the default first target data is TEXT, and the second target data is a numeric type.

In some embodiments of the present application, the business data will change during the use of the Activiti business flow engine 100 , for example, when a new policy is generated, there will be a new policy identifier, policyholder, and so on. When both the first target data and the second target data are business data, the above optimization includes when the data structure 400 is written into the search engine 300, based on the field type of the first target data or the second target data, the first target data and The second target data specifies the data type to obtain the target data structure 500 . For example, the first target data is a text field, and the second target data is a numeric field. When the data structure 400 is written into the search engine 300, the first target data is automatically designated as TEXT, and the second target data is designated as a numeric type.

In some embodiments of the present application, the search engine 300 is Elasticsearch or Solr. Among them, Elasticsearch is a distributed, highly scalable, and high-real-time search and data analysis engine, which facilitates fast and efficient data search and improves the business processing performance of the Activiti business flow engine 100 . At the same time, compared with building a high-performance database 200, the cost of building an Elasticsearch environment with similar performance is much lower and more economical. The performance of the Activiti business flow engine 100 is greatly improved at a lower cost.

In some embodiments of the present application, when the Activiti business flow engine 100 uses the search engine 300 to query task data or process data, the query conditions are directly converted into query syntax of the search engine 300 and sent to the search engine 300 .

In some embodiments of the present application, when the Activiti business flow engine 100 utilizes the search engine 300 to query business data, the query conditions include: equal to, interval (comprising greater than, less than, greater than or equal to, less than or equal to), fuzzy matching; query condition value Field type: text or other non-text types, non-text types include data, date, etc. If query condition is " equal to or interval+non-text type ", then convert into the query syntax of corresponding search engine 300; If query condition is " fuzzy match+text type ", then convert into the query syntax of search engine 300, search engine 300 Word segmentation queries will be performed automatically. If the query condition is "fuzzy match + non-text type", an error will be reported. If the query condition is "equal to or interval + text type", add ".keyword" (do not use word segmentation to search the text) after the condition field name to convert it into the query syntax of the search engine 300 . If there are multiple query conditions, the default relationship between the conditions is "AND (AND)", or an "or (OR)" relationship can be specified.

Since the Activiti business flow engine 100 itself does not have distributed transaction processing capabilities, when the Activiti business flow engine 100 is used as a component in a distributed system, additional functions are required to maintain data between the Activiti business flow engine 100 and other components of the system Consistency and completeness. For example, when the Activiti business flow engine 100 provides services as a cloud service component, it is not in the same system as other components in the cloud service system. When the Activiti business flow engine 100 and other cloud service components need to perform business Data consistency and integrity between the Activiti business flow engine 100 and the other service components to ensure the normal operation of the cloud service.

Based on the above problems, in some embodiments of the present application, the search engine 300 is a distributed search engine 300 . Specifically, the search engine 300 is a distributed search engine, and the Activiti business flow engine 100 is integrated into the distributed transaction framework to realize the consistency and consistency between the Activiti business flow engine 100 and the search engine 300 at least about the first target data and the second target data integrity. Ensure that the data written into the database 200 by the Activiti business flow engine 100 is consistent with the data written into the search engine 300 .

In some embodiments of the present application, the above-mentioned distributed transaction framework is one of Seata, LCN, and EasyTransaction.

In some embodiments of the present application, the first target data and the second target data are acquired based on the number of visits or data volume of the Activiti business flow engine 100 . For example, when the Activiti business flow engine 100 is running normally for a period of time, and the operations of accessing the process identification and task identification exceed one thousand times, the process identification and task identification are respectively determined as the first target data and the second target data.

In some embodiments of the present application, the first target data and the second target data include at least one of business-related task data, business-related business data, and business-related process data delivery.

In some embodiments of the present application, the business includes at least one of insurance business-related preliminary examination of orders, new order registration, entry review, underwriting, registration and policy sending.

The present application provides a computer program product, the computer program product includes instructions, and the instructions are used to implement the above data processing method.

FIG. 7 is a block diagram of an electronic device according to an embodiment of the present application. Fig. 7 schematically illustrates an example electronic device 70 according to various embodiments. In one embodiment, the electronic device 70 may include one or more processors 701, a system control logic 702 connected to at least one of the processors 701, a system memory 703 connected to the system control logic 702, and a system control logic 702 connected to the system control logic 702. A non-volatile memory (NVM) 704 is connected, and a network interface 706 is connected to the system control logic 702 .

In some embodiments, processor 701 may include one or more single-core or multi-core processors. In some embodiments, the processor 701 may include any combination of general-purpose processors and special-purpose processors (eg, graphics processors, application processors, baseband processors, etc.). In the embodiment in which the electronic device 70 adopts an eNB (Evolved Node B, enhanced base station) or a RAN (Radio Access Network, radio access network) controller, the processor 701 may be configured to execute various consistent embodiments, for example , one or more of the multiple embodiments shown in FIGS. 1-6 . For example, processing 701 may be used to execute the data processing method described above.

In some embodiments, system control logic 702 may include any suitable interface controller to provide any suitable interface to at least one of processors 701 and/or any suitable device or component in communication with system control logic 702 .

In some embodiments, system control logic 702 may include one or more memory controllers to provide an interface to system memory 703 . System memory 703 can be used to load and store data and/or instructions. Memory 703 of system 70 may in some embodiments include any suitable volatile memory, such as a suitable dynamic random access memory (DRAM).

NVM/memory 704 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. In some embodiments, NVM/memory 704 may include any suitable non-volatile memory such as flash memory and/or any suitable non-volatile storage device, such as HDD (Hard Disk Drive, hard disk drive), CD (Compact Disc , CD) drive, DVD (Digital Versatile Disc, Digital Versatile Disc) drive at least one.

NVM/memory 704 may comprise a portion of storage resources on the device on which electronic device 70 is installed, or it may be accessed by, but not necessarily part of, the device. For example, NVM/storage 704 may be accessed over a network via network interface 706 .

In particular, system memory 703 and NVM/storage 704 may include, respectively, temporary and permanent copies of instructions 705 . The instructions 705 may include: instructions that cause the electronic device 70 to implement the method shown in FIG. 1 when executed by at least one of the processors 701. In some embodiments, instructions 705 , hardware, firmware and/or software components thereof may additionally/alternatively reside in system control logic 702 , network interface 706 and/or processor 701 .

The network interface 706 may include a transceiver for providing a radio interface for the electronic device 70 to communicate with any other suitable devices (such as front-end modules, antennas, etc.) through one or more networks. In some embodiments, the network interface 706 may be integrated with other components of the electronic device 70 . For example, the network interface 706 may be integrated into at least one of the processor 701, the system memory 703, the NVM/storage 704, and a firmware device (not shown) with instructions, when at least one of the processor 701 executes the When instructed, the electronic device 70 implements the methods shown in the above method embodiments.

Network interface 706 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface. For example, network interface 706 may be a network adapter, a wireless network adapter, a telephone modem and/or a wireless modem.

The electronic device 70 may further include: an input/output (I/O) device 707 . The I/O device 707 may include a user interface, enabling the user to interact with the electronic device 70 ; the design of the peripheral component interface enables the peripheral component to also interact with the electronic device 70 .

The present application also provides a readable medium, on which instructions are stored, and when the instructions are executed on the electronic equipment, the electronic equipment executes the above-mentioned data processing method.

The present application also provides an electronic device, the electronic device includes a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the processors of the electronic device, is used for executing the above-mentioned data processing method.

In the description provided herein, numerous specific details are set forth. However, it is understood that the embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Similarly, it should be understood that in the above description of exemplary embodiments of the application, in order to streamline the application and to facilitate understanding of one or more of the various inventive aspects, various features of the application are sometimes grouped together in a single embodiment, figure, or its description. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following this Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this application.

Those skilled in the art can understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. Modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

In addition, those skilled in the art will appreciate that although some embodiments described herein include some features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the present application. and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

Claims (12)

A kind of data processing method, described method is based on Activiti business flow engine and search engine, described Activiti business flow engine uses database, and described database contains the relevant data of business process, it is characterized in that, described method comprises: The Activiti business flow engine obtains at least the first target data and the second target data from the database based on business requirements; combining at least the first object data and the second object data to generate a data structure, wherein the data structure includes a relationship description file of the first object data and the second object data; The data structure is written into the search engine, so that the Activiti service flow engine can obtain the data structure by accessing the search engine. The method according to claim 1, wherein the data structure is written into the search engine so that the Activiti business flow engine obtains the data structure by accessing the search engine ,include: Optimizing the data structure based on the search engine to obtain a target data structure, wherein the text of the target data structure supports the search conditions of the search engine; Writing the target data structure into the search engine, so that the Activiti service flow engine can obtain the target data structure by accessing the search engine. The method according to claim 2, characterized in that, The text includes data types and/or formats. The method according to any one of claims 1 to 3, characterized in that, The search engine is a distributed search engine; The Activiti business flow engine integrates a distributed transaction framework to achieve consistency and integrity between the Activiti business flow engine and the search engine at least with respect to the first target data and the second target data. The method according to claim 4, characterized in that, The distributed transaction framework is one of Simple Extensible Autonomous Transaction Architecture, Local Communication Network, and EasyTransaction. The method according to any one of claims 1 to 3, wherein the method further comprises: The first target data and the second target data are acquired based on the number of visits or data volume of the Activiti service flow engine. The method according to any one of claims 1 to 3, characterized in that, The search engine is Elasticsearch or Solr. The method according to any one of claims 1 to 3, characterized in that, The first target data and the second target data include at least one of task data related to the business, business data related to the business, and process data transfer related to the business. The method according to claim 8, characterized in that, The business includes at least one of insurance business-related initial review of orders, new order registration, entry review, underwriting, registration of documents and policy delivery. A computer program product, characterized in that the computer program product includes instructions, and the instructions are used to implement the data processing method according to any one of claims 1 to 9. A readable medium, characterized in that instructions are stored on the readable medium, and when the instructions are executed on the electronic equipment, the electronic equipment executes the data processing according to any one of claims 1 to 9 method. An electronic device, characterized in that it comprises: a memory for storing instructions to be executed by the one or more processors of the electronic device, and The processor is one of the processors of the electronic device, configured to execute the data processing method according to any one of claims 1-9.

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