CN115034512A - A process optimization method, system, device and computer-readable storage medium - Google Patents

Abstract

The invention belongs to the technical field of computer workflow and business process management, and in particular relates to a business process optimization method, system, device and computer-readable storage medium. The method includes: a process data collection client obtains process execution data information from a process server, and sends the information to a message middleware. The process pipeline program obtains process execution data from the subscribed message middleware process queue, and saves the process execution data to the Elasticsearch server. The main program of the process warehouse receives user requests, obtains process data, performs statistical analysis and processing on process log information according to business process KPI indicators, saves the analysis and processing results to the Elasticsearch server, and displays relevant data on the dashboard interface. By building a process warehouse, the invention centrally processes and stores the process data and execution log information of the process server, provides strong process execution efficiency data support for process optimization, and has broad application prospects.

Description

A process optimization method, system, device and computer-readable storage medium

technical field

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

Background technique

With the research of process theory such as process reengineering, more and more enterprise IT systems increase the support for business processes by citing workflow middleware. On the other hand, with the popularity of open source software, open source workflow software such as Activiti and Camunda are also adopted by more and more enterprises. Usually the business process management life cycle generally refers to the four basic stages of business process management: process modeling, execution, analysis and optimization.

Although process tools such as Activiti and Camunda provide tool support for process modeling and execution of process tasks, they do not provide analysis and optimization tools. For process analysts, since there is no corresponding process analysis tool, it is necessary to analyze the operation system and process execution of the process system according to the process log information collected in the operation and maintenance system. Since the log centralized processing system only provides log general processing and analysis tools, and traditional log analysis tools cannot interpret the BPMN model and other process professional data information in the log information, the analysis and statistics of the process data in the log requires a lot of development and maintenance work. . At present, there is no solution for process analysis of process log data, the potentially valuable process execution data in process execution logs has not been exploited, and the potential of process optimization has not been fully utilized.

SUMMARY OF THE INVENTION

Aiming at the shortcomings that the existing log data analysis cannot provide a process analysis-oriented technical solution, the present invention proposes a process optimization method, device, equipment and computer storage medium. The data is associated with the process KPI indicators, and the KPI execution result data is displayed through the visual flowchart analysis dashboard, which makes the process analysis process more concise and clear.

To achieve the above object, the present invention has adopted the following technical solutions:

According to a first aspect of the present invention, a process optimization method is provided. The method includes:

The process data collection client obtains process execution data information from the process server, and sends the data information to the message middleware through the message middleware process queue.

The process pipeline program obtains the process execution data run by the process server from the subscribed message middleware process queue, and saves the process execution data in the process storage unit.

The main program of the process warehouse receives user requests, retrieves the process execution data of the process, summarizes, counts and analyzes the process execution data according to the KPI indicators of the business process, saves the statistical analysis results in the process storage unit, and displays them in the process warehouse instrument. The disk combined with the flow chart displays the relevant statistical analysis results in an intuitive way. The process execution data includes process log information and other data. The process data storage unit is an Elasticsearch server. The process warehouse dashboard is used to graphically display process KPI execution analysis results.

The above aspects and any possible implementation manners further provide an implementation manner, wherein the process execution data includes process definitions, process instances, process activities, process tasks, process variables, process events, and process execution log data.

According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein the message middleware process queue includes a publish/subscribe queue set in the message server for data transmission of the process server.

According to the above aspect and any possible implementation manner, an implementation manner is further provided, where the message middleware is the message middleware of Kafka or RabbitMQ.

The above aspects and any possible implementations further provide an implementation, wherein the business process KPI indicators include process indicators, activity indicators and task indicators; the process indicators include process instance count and process instance execution time; The activity indicator includes activity instance count and activity instance execution time; the task indicator includes task count and task execution time.

The above aspects and any possible implementation manners further provide an implementation manner in which the process execution data is aggregated, counted and analyzed according to the business process KPI indicators, including:

The flow chart activity node on the flow instance graph page visually displays the execution of process KPI indicators in the form of numbers or heat maps, supports the comparative analysis of the execution of different processes and activities, and collects statistics on the execution of process instances and activities.

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

Process data collection client, message server, process pipeline program unit, process data storage unit, process warehouse main program unit and process analysis dashboard.

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

The message server is used to obtain the process execution data information collected by the process data collection client by using the message middleware, and send the information to the process pipeline program unit.

The process pipeline program unit is used to automatically acquire process execution data from the process queue of the message middleware in the manner of regular polling, and save the data to the process storage unit (Elasticsearch server).

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

The main program unit of the process warehouse summarizes, analyzes and processes the process execution data according to the process KPI indicators.

The process analysis dashboard is used to graphically display the summary analysis results of the process execution data.

According to a third aspect of the present invention, an electronic device is provided. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned method when the processor executes the 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, implements a method according to the first and/or second aspect of the present invention .

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

(1) The present invention centrally stores and processes the data and log information of the process server by building a process warehouse, which can provide strong data support for process optimization and has broad application prospects.

(2) In the present invention, the process warehouse supports the centralized and unified analysis of the multi-instance process server in the cloud environment, and the execution data of multiple process server programs is unified and centralized through the message middleware, and the process pipeline program is stored in the process storage unit ( Elasticsearch server), analyzes and counts process data based on process instance KPIs and activity instance KPIs, helps users identify bottlenecks in process execution, and provides strong data support for subsequent optimization of the process. The process optimization system in the present invention runs in a container mode, has no intrusion to process server programs and business systems, supports free combination with business system KPIs, greatly improves the efficiency of process analysis, and further exerts the role and potential of process services.

Description of drawings

Fig. 1 is the method flow chart of the process optimization method in the present invention;

Fig. 2 is the block diagram of the process optimization system in the present invention;

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

Detailed ways

In order to make the purposes, 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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In addition, the term "and/or" in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, A and B exist at the same time, There are three cases of B alone. In addition, the character "/" in this document generally indicates that the related objects are an "or" relationship.

As shown in FIG. 1 and FIG. 2 , the present invention proposes a process optimization method 100 and a system. By building a process warehouse, the log data of process execution is associated with the KPI indicators of the process, and the dashboard is displayed through a visual process diagram. The execution data of KPI is displayed.

In block 110, the process data collection client collects process data. The process data collection client communicates with the process server through the REST service interface or JDBC database connection, obtains the process execution data information from the process server by means of regular polling, and sends the received data to the backend in json format. message middleware. The process execution data information includes process definition, process instance, process activity, process task, process variable and process event data. Process data collection and communication support adopts REST/JDBC mode, which is flexible and diverse, and can collect data without intrusion.

In block 120, the process pipeline program obtains the process data from the preset message middleware process subscription channel channel, the message middleware is Kafka by default, and also supports the RabbitMQ message middleware, and after obtaining the process execution data, the process execution data is saved to the backend. In the Elasticsearch server, it is used for later process analysis and retrieval. Kafka message middleware is used for message communication, which supports clustering and concurrent collection of large-scale process data. The data storage adopts the Elasticsearch server, which supports horizontal expansion in a cluster mode, which is not limited by the capacity of traditional databases, supports the storage and processing of massive log data, supports real-time analysis and processing of data, and supports third-party tools for data display and retrieval.

In block 130, the main program of the process warehouse is responsible for receiving the user's request, acquiring process data, saving the process data to the Elasticsearch server at the back end, and analyzing and making statistics on the process log information according to the indicators.

The default business process KPI indicators include: process indicators, activity indicators, and task indicators. The process indicators include: process instance count (total, success count, failure count), and process instance execution time (including shortest, longest, and average). The activity indicators include: active instance count (total, successful count, failure count), and active instance execution time (including the shortest, longest, and average). The task indicators include: task count (total, successful count, failure count), task execution time (including shortest, longest, average).

Finally, the process dashboard program is responsible for displaying the execution of process KPI indicators in the form of numbers or heat maps on the activity node of the flowchart on the process instance diagram page, and provides a comparative analysis of the execution between different processes and activities, and provides the process Statistics on the execution of instances and activities. Provide real-time analysis and display of the process based on the flow chart, so that users can see the execution efficiency of the process at a glance. At the same time, it supports data drill-down to facilitate the understanding of the specific content of the problem; it supports the comparative analysis of KPI indicators of different process instances. Display process, activity, and task execution information (execution count statistics and execution time statistics, etc.) in a digital or heat map manner.

Figure 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present invention. As shown in FIG. 3 , device 300 includes CPU 301 that can perform various appropriate actions and processes according to computer program instructions stored in ROM 302 or loaded into RAM 303 from storage unit 308 . In the RAM 303, various programs and data necessary for the operation of the device 300 can also be stored. The CPU 301 , the ROM 302 , and the RAM 303 are connected to each other through a bus 304 . I/O interface 305 is also connected to bus 304 .

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

Processing unit 301 performs the various methods and processes described above, eg, method 100 . For example, in some embodiments, method 100 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 308 . In some embodiments, part or all of the computer program may be loaded and/or installed on device 300 via ROM 302 and/or communication unit 309 . When a computer program is loaded into RAM 303 and executed by CPU 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the CPU 301 may be configured to perform the method 100 by any other suitable means (eg, by means 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 Chips (SOCs), Complex Programmable Logic Device (CPLD) and so on.

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 code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package 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 may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, RAM, ROM, EPROM, optical fibers, CD-ROMs, optical storage devices, magnetic storage devices, or the foregoing any suitable combination.

The present invention is applied to an intelligent manufacturing upgrade project, which has a total of 100,000 process instances, and each process contains 20 nodes on average. Through the process warehouse, the average execution time, longest execution time, shortest execution time, average execution time of manual tasks, etc. Process execution information. The present invention can also provide the summary data of the approval process and the approval disapproval process, and these data include the average execution time of the process and the average execution time of each activity. The present invention greatly improves the efficiency of process analysis and saves time for data processing.

The above-mentioned embodiments are only to describe the preferred embodiments of the present invention, but not to limit the scope of the present invention. On the premise of not departing from the design spirit of the present invention, various modifications made by those of ordinary skill in the art to the technical solutions of the present invention and improvements, all should fall within the protection scope determined by the claims of the present invention.

Claims (10)

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.

Images