CN115034512A

CN115034512A - Process optimization method, system, equipment and computer readable storage medium

Info

Publication number: CN115034512A
Application number: CN202210787727.3A
Authority: CN
Inventors: 吴庆建; 薛爱梅
Original assignee: Shandong Sport University
Current assignee: Shandong Sport University
Priority date: 2022-07-04
Filing date: 2022-07-04
Publication date: 2022-09-09

Abstract

The invention belongs to the technical field of computer workflow and business process management, and particularly relates to a business process optimization method, a system, equipment and a computer readable storage medium. The method comprises the following steps: the process data acquisition client acquires process execution data information from the process server and sends the information to the message middleware. And the flow pipeline program acquires the flow execution data from the subscribed message middleware flow queue and stores the flow execution data into the Elasticissearch server. The flow warehouse main program receives a user request, acquires flow data, performs statistical analysis processing on flow log information according to a service flow KPI (Key performance indicator), stores an analysis processing result into an Elasticissearch server and displays related data on an instrument panel interface. The invention carries out centralized processing and storage on the process data and the execution log information of the process server by building the process warehouse, provides powerful process execution efficiency data support for process optimization, and has wide application prospect.

Description

Flow optimization method, system, equipment and computer readable storage medium

Technical Field

The invention relates to the technical field of computer workflow and business process management, in particular to a process optimization method, a device, equipment and a computer readable storage medium.

Background

With the research of flow theories such as flow reconstruction and the like, more and more enterprise IT systems increase the support of the systems on business flows by introducing workflow middleware. On the other hand, with the popularity of open-source software, open-source workflow software such as Activiti, Camunda is also adopted by more and more enterprises. Generally, a business process management lifecycle generally refers to four basic phases of business process management: process modeling, execution, analysis, and optimization.

While flow tools such as activti, Camunda provide tool support for flow modeling and executing flow tasks, no analysis and optimization tools are provided. For a process analyst, since there is no corresponding process analysis tool, the operation system condition and the process execution condition of the process system need to be analyzed according to the process log information collected in the operation and maintenance system in a centralized manner. Because the log centralized processing system only provides a general log processing and analyzing tool, and the traditional log analyzing tool cannot explain data information in the process professional field such as a BPMN (business process model) in log information, a large amount of development and maintenance work is required for analyzing and counting the process data in the log. At present, no solution for flow-oriented analysis is available for flow log data, potentially valuable flow execution data in the flow execution log is not explored and used, and the potential of flow optimization is not fully utilized.

Disclosure of Invention

Aiming at the defects of the technical scheme that the existing log data analysis cannot provide the flow analysis, the invention provides a flow optimization method, a flow optimization device, a flow optimization equipment and a computer storage medium.

In order to achieve the purpose, the invention adopts the following technical scheme:

according to a first aspect of the present invention, a process optimization method is provided. The method comprises the following steps:

the process data acquisition client acquires process execution data information from the process server and sends the data information to the message middleware through the message middleware process queue.

And the flow pipeline program acquires the flow execution data operated by the flow server from the subscribed message middleware flow queue and stores the flow execution data into the flow storage unit.

The flow warehouse main program receives a user request, calls flow execution data of a flow, collects, counts and analyzes the flow execution data according to a service flow KPI (Key Performance indicator), stores a statistical analysis result into a flow storage unit, and displays a related statistical analysis result in a visual mode by combining a flow chart with a flow warehouse instrument panel. The flow execution data includes data such as flow log information. The process data storage unit is an Elasticsearch server. And the process warehouse instrument panel is used for graphically displaying the execution analysis result of the process KPI.

The above-described aspects and any possible implementations further provide an implementation in which the process execution data includes process definitions, process instances, process activities, process tasks, process variables, process events, and process execution log data.

The above-described aspects and any possible implementations further provide an implementation in which the message middleware process queue includes a publish/subscribe queue set in the message server for data transmission to the process server.

There is further provided an implementation of the above-described aspect and any possible implementation in which the message middleware is of Kafka or RabbitMQ.

The above aspects and any possible implementation manners further provide an implementation manner, where the business process KPI indicators include a process indicator, an activity indicator, and a task indicator; the process indexes comprise process instance counts and process instance execution times; the activity metrics include an activity instance count and an activity instance execution time; the task index includes a task count and a task execution time.

The above-described aspect and any possible implementation manner further provide an implementation manner, where the summarizing, counting, and analyzing the process execution data according to the KPI indicator of the business process includes:

and (3) visually displaying the execution condition of the KPI (Key performance indicator) of the process by using a digital or thermodynamic diagram mode at the activity node of the process example diagram page, supporting the comparative analysis of the execution conditions of different processes and activities, and counting the execution conditions of the process example and the activities.

According to a second aspect of the invention, a process optimization system is provided. The system comprises:

the system comprises a process data acquisition client, a message server, a process pipeline program unit, a process data storage unit, a process warehouse main program unit and a process analysis instrument panel.

The process data acquisition client is used for acquiring process execution data information from the process server.

And the message server is used for acquiring the flow execution data information acquired by the flow data acquisition client by using the message middleware and sending the information to the flow pipeline program unit.

The flow pipeline program unit is used for automatically acquiring flow execution data from the flow queue in a timed polling manner, and storing the data in a flow storage unit (elastic search server).

The flow data storage unit is used for storing flow execution data.

And the flow warehouse main program unit is used for summarizing and analyzing the flow execution data according to the flow KPI.

The flow analysis instrument panel is used for graphically displaying a summary analysis result of the flow execution data.

According to a third aspect of the invention, an electronic device is provided. The electronic device comprises a memory having stored thereon a computer program and a processor implementing the method as described above when executing said program.

According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs a method as according to the first and/or second aspect of the present invention.

Compared with the prior art, the invention has the following beneficial effects:

(1) the invention can intensively store and process the data and the log information of the flow server by building the flow warehouse, can provide powerful data support for flow optimization, and has wide application prospect.

(2) In the invention, the flow warehouse supports the centralized and unified analysis of the multi-instance flow server in the cloud environment, the execution data of a plurality of flow server programs are uniformly centralized through the message middleware, the execution data are stored to a flow storage unit (elastic search server) through a flow pipeline program, and the flow data are analyzed and counted based on the flow instance KPI and the activity instance KPI, so that a user is helped to find out the bottleneck of flow execution, and powerful data support is provided for the subsequent optimization of the flow. The flow optimization system in the invention operates in a container mode, has no invasion to a flow server program and a business system, supports free combination with a business system KPI, can greatly improve the efficiency of flow analysis, and further plays a role and potential of flow service.

Drawings

FIG. 1 is a process flow diagram of a process optimization method of the present invention;

FIG. 2 is a block diagram of a process optimization system of the present invention;

FIG. 3 is a block diagram of an exemplary electronic device capable of implementing embodiments of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

As shown in fig. 1 and fig. 2, the present invention provides a process optimization method 100 and a system, which associate log data of process execution with KPI indicators of a process by building a process warehouse, and display execution data of KPI by using a visual process diagram display dashboard.

In block 110, a process data collection client collects process data. The process data acquisition client communicates with the process server in a REST service interface mode or a JDBC database connection mode, acquires process execution data information from the process server in a timing polling mode, and sends the received data to a message middleware at the back end in a json format. The process execution data information includes process definitions, process instances, process activities, process tasks, process variables, and process event data. The flow data acquisition communication support adopts an REST/JDBC mode, is flexible and various, and can acquire data without invasion.

In block 120, the flow pipeline program acquires flow data from a preset flow subscription channel of the message middleware, the message middleware defaults to Kafka and also supports RabbitMQ message middleware, and after acquiring the flow execution data, the flow execution data is stored in an Elasticsearch server at the back end for later use in flow analysis and retrieval. And a Kafka message middleware is adopted to carry out a message communication carrier, support a cluster mode and support concurrent acquisition of large-scale process data. The data storage adopts an elastic search server, supports horizontal extension of a cluster mode, is not limited by the capacity of a traditional database, supports storage and processing of mass log data, supports real-time analysis and processing of data, and supports data display and retrieval of a third-party tool.

In block 130, the process warehouse main program is responsible for receiving the request of the user, obtaining the process data, storing the process data in the backend elastic search server, and analyzing and counting the process log information according to the index.

The default service process KPI indexes include: a process index, an activity index, and a task index. The process indexes include: flow instance count (total, success count, failure count), flow instance execution time (including shortest, longest, average). The activity metrics include: active instance count (total, success count, failure count), active instance execution time (including shortest, longest, average). The task indexes include: task count (total, success count, failure count), task execution time (including shortest, longest, average).

And finally, the flow instrument panel program is responsible for displaying the execution condition of the KPI in the flow by means of numbers or thermodynamic diagrams on the activity nodes of the flow chart of the flow example chart page, providing comparative analysis of the execution condition among different flows and activities, and providing statistics of the execution condition of the flow example and the activities. Providing real-time flow analysis and display based on the flow chart, so that a user can clearly know the execution efficiency of the flow, and simultaneously, data drilling is supported, and the specific content of a problem is conveniently known; the KPI indexes of different process examples are compared and analyzed, and the execution information (execution count statistics, execution time statistics and the like) of the processes, activities and tasks is displayed on the flow chart of the flow instrument panel in a digital or thermodynamic diagram mode.

FIG. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement an embodiment of the invention. As shown in fig. 3, the apparatus 300 includes a CPU301 that can perform various appropriate actions and processes according to computer program instructions stored in a ROM302 or computer program instructions loaded from a storage unit 308 into a RAM 303. In the RAM303, various programs and data necessary for the operation of the device 300 can also be stored. The CPU301, ROM302, and RAM303 are connected to each other via a bus 304. An I/O interface 305 is also connected to bus 304.

Various components in device 300 are connected to I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, or the like; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processing unit 301 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 300 via ROM302 and/or communication unit 309. When the computer program is loaded into RAM303 and executed by CPU301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the CPU301 may be configured to perform the method 100 by any other suitable means (e.g., by way of firmware).

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The method is applied to an intelligent manufacturing upgrading project, the project totals 10 ten thousand process examples, and each process comprises 20 nodes on average. And the flow warehouse can respectively count the related flow execution information such as the average execution time, the longest execution time, the shortest execution time, the average execution time of the manual task and the like of each flow task node, flow automatic task node and manual task node within 3 seconds. The invention can also provide summarized data of approved and disapproved processes, which include the average execution time of the process and the average execution time of each activity. The invention greatly improves the efficiency of flow analysis and saves the time of data processing.

The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.

Claims

1. A method for process optimization, comprising:

the process data acquisition client acquires process execution data information from the process server and sends the data information to the message middleware through a message middleware process queue;

the flow pipeline program acquires flow execution data operated by the flow server from the subscribed message middleware flow queue and stores the flow execution data into a flow storage unit;

the flow warehouse main program receives the user request, calls flow execution data of the flow, collects, counts and analyzes the flow execution data according to the KPI (Key Performance indicator) of the service flow, stores the statistical analysis result into a flow storage unit, and displays the relevant statistical analysis result in the flow warehouse instrument panel in combination with the flow chart.

2. The process optimization method of claim 1, wherein the process execution data includes process definitions, process instances, process activities, process tasks, process variables, process events, and process execution log data.

3. The process optimization method of claim 1 wherein the message middleware process queue comprises a publish/subscribe queue in the message server set for process server data transmission.

4. The process optimization method of claim 1, wherein the message middleware is Kafka or RabbitMQ message middleware.

5. The process optimization method according to claim 1, wherein the process storage unit is an Elasticsearch server.

6. The process optimization method according to claim 1, wherein the business process KPI indicators comprise process indicators, activity indicators and task indicators; the process indexes comprise process instance counts and process instance execution times; the activity metrics include an activity instance count and an activity instance execution time; the task index includes a task count and a task execution time.

7. The process optimization method according to claim 1, wherein the process execution data is summarized, counted and analyzed according to a service process KPI indicator, and includes:

and (3) visually displaying the execution condition of the KPI (Key performance indicator) of the process by using a digital or thermodynamic diagram mode at the activity node of the process example diagram page, comparing and analyzing the execution conditions of different processes and activities, and counting the execution conditions of the process example and the activities.

8. A process optimization system, comprising:

the system comprises a process data acquisition client, a message server, a process pipeline program unit, a process data storage unit, a process warehouse main program unit and a process analysis instrument panel;

the process data acquisition client is used for acquiring process execution data information from the process server;

the message server is used for acquiring the process execution data information acquired by the process data acquisition client by using the message middleware and sending the information to the process pipeline program unit;

the flow pipeline program unit is used for automatically acquiring flow execution data from the flow queue acquired by the message middleware in a timing polling mode and storing the data in the flow storage unit;

the flow data storage unit is used for storing flow execution data.

And the flow warehouse main program unit acquires the flow data in the flow data storage unit, summarizes and analyzes the flow execution data according to the flow KPI index, stores the flow KPI execution analysis result in the flow data storage unit and returns the flow KPI execution analysis result to the flow analysis instrument panel.

And the flow analysis instrument panel graphically displays the analysis result executed by the flow KPI.

And the process analysis instrument panel is used for graphically displaying the analysis result executed by the process KPI.

9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.