Process optimization method, electronic equipment, storage medium and program product

Abstract

The invention provides a process optimization method, electronic equipment, a storage medium and a program product, and relates to the technical field of financial science and technology. The method comprises the steps of obtaining process log data of a to-be-optimized business process; determining a core field required by process mining corresponding to the to-be-optimized business process and a monitoring index corresponding to the to-be-optimized business process; a process mining model is applied to perform process mining on the process log data based on the core field to obtain a process mining result for the to-be-optimized business process, and the process mining model is used for mining a process path and optimizing the process path; and obtaining a process optimization report of the to-be-optimized business process based on the process mining result and the monitoring index. Through the method and the device, the effects of quickly and effectively inspecting the business process and providing a reliable basis for business process optimization can be achieved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a process optimization method, electronic device, storage medium and program product. Background Art

[0002] At present, in scenarios with large business volumes and complex business processes, different business types correspond to different business processes, and the process paths are long. For example, in banks, business processes often have many flow nodes and long flow cycles. Faced with massive amounts of business process data, traditional consulting and research methods are time-consuming and labor-intensive, and are unable to effectively review the actual business process flow, which in turn makes it impossible to identify and resolve the bottlenecks and difficulties in the business process, making it difficult to effectively promote business process optimization and efficiency improvement.

[0003] Therefore, how to quickly and effectively review business processes and provide a reliable basis for business process optimization has become an urgent issue to be addressed. Summary of the invention

[0004] The embodiments of the present application provide a process optimization method, an electronic device, a storage medium, and a program product to achieve the effect of quickly and effectively reviewing business processes and providing a reliable basis for business process optimization.

[0005] In a first aspect, an embodiment of the present application provides a process optimization method, comprising:

[0006] Obtain process log data of the business process to be optimized;

[0007] Determine the core fields required for process mining corresponding to the business process to be optimized, as well as the monitoring indicators corresponding to the business process to be optimized;

[0008] Apply the process mining model to conduct process mining on process log data based on core fields to obtain process mining results for the business process to be optimized. The process mining model is used to mine process paths and optimize them.

[0009] Based on process mining results and monitoring indicators, obtain a process optimization report for the business process to be optimized.

[0010] In a possible implementation, the process mining model is specifically used for:

[0011] Filter the data in the process log data that does not conform to the business logic, and obtain the first data that conforms to the business logic;

[0012] Applying a stream processing technology to analyze the first data, obtaining a directed graph corresponding to the first data, wherein the directed graph includes a process path;

[0013] A visual layout algorithm is applied to remove loops in the directed graph, and nodes in the directed graph are assigned to levels to identify parallel paths. The internal nodes of each level are sorted, and the coordinates of each sorted node are designed to obtain process mining results.

[0014] In a possible implementation, the process mining model also has at least one of the following functions:

[0015] Modular design;

[0016] A standardized interface is provided;

[0017] There are multiple process mining algorithms built in;

[0018] A parameter configuration interface is provided to configure parameters of the process mining model through the parameter configuration interface.

[0019] In a possible implementation, the process optimization further includes:

[0020] After obtaining the process mining results for the business process to be optimized, the process mining results are displayed based on the set display type.

[0021] In a possible implementation, the process optimization method further includes:

[0022] Based on the process optimization report, use artificial intelligence technology to optimize the business processes to be optimized.

[0023] In a possible implementation, the process log data is obtained in the following manner:

[0024] Collect all relevant log data from the business systems of the business processes to be optimized;

[0025] The collected log data is cleaned, desensitized, and integrated to obtain the process log data of the business process to be optimized and form a process data mart;

[0026] Obtaining process log data of the business process to be optimized, including: obtaining the process log data of the business process to be optimized from a process data mart.

[0027] In a second aspect, an embodiment of the present application provides a process optimization device, including:

[0028] An acquisition module is used to obtain process log data of the business process to be optimized;

[0029] A determination module, used to determine the core fields required for process mining corresponding to the business process to be optimized, and the monitoring indicators corresponding to the business process to be optimized;

[0030] A processing module is used to apply a process mining model to perform process mining on the process log data based on the core fields to obtain process mining results for the business process to be optimized, wherein the process mining model is used to mine process paths and identify parallel paths in the process paths; and, based on the process mining results and the monitoring indicators, obtain a business process optimization report for the business process to be optimized.

[0031] In a third aspect, an embodiment of the present application provides a process optimization device, including: a memory, a processor;

[0032] The memory stores computer-executable instructions;

[0033] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0035] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0036] The process optimization method, electronic device, storage medium and program product provided in the embodiments of the present application use a process mining model to mine the process path of the business to be optimized from the process log data, and combine it with monitoring indicators to obtain a process optimization report for the business to be optimized, thereby providing a reliable reference for the process optimization of the business. In the design of the process mining model, the identification of parallel paths in the process path is strengthened, so as to achieve a clear and accurate presentation of the business process, thereby providing a basis for the optimization of the business process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] Figure 1 Schematic diagram of the process optimization method provided for this application Figure 1 ;

[0039] Figure 2 Schematic diagram of the process optimization method provided for this application Figure 2 ;

[0040] Figure 3A schematic diagram of the process mining model structure provided for this application;

[0041] Figure 4 Schematic diagram of the process optimization method provided for this application Figure 3 ;

[0042] Figure 5 A schematic diagram of the structure of the process optimization device provided in this application;

[0043] Figure 6 This is a schematic diagram of the structure of the process optimization equipment provided in this application.

[0044] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0045] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0046] The process of completing a complete business behavior with two or more business steps is called a process. Usually, different business types correspond to different business processes. In scenarios with many business types and complex businesses, the flow of business processes is often long and complex. For example, in banking scenarios, a business usually involves multi-party collaboration, and the business process is characterized by many flow nodes and a long flow cycle.

[0047] In order to ensure the normal operation of the business, it is necessary to effectively review the business process. Usually, there are a large number of process events generated by the business every day. The traditional way of investigating the process flow through consultation and investigation is time-consuming and laborious. It is difficult to clearly summarize and present the actual business process flow. Therefore, it is impossible to effectively review the business process, and it is difficult to find the difficulties and bottlenecks in the business execution process.

[0048] The present application provides a process optimization method, which uses a process mining model to perform process mining on the process log data of the business to be optimized, obtains the actual flow of the business process from the massive process log data, sets monitoring indicators, monitors the process mining results, promptly discovers problems in the process flow, forms a process optimization report, and provides a reliable basis for the optimization of business processes. In the setting of the process mining model, the process mining algorithm is optimized, and the optimization processing of the process path is strengthened so that the process can be presented more clearly and concisely. In addition, a modular design is carried out for the process mining model, and a standardized interface and a parameter configuration interface are set. On the basis of being able to connect to various types of data sources and being reusable, it can be expanded through user-defined parameters.

[0049] Optionally, the process optimization method is applied to a process optimization system platform. Users set the business processes and indicators they want to observe on the process optimization system platform, obtain process mining results and process optimization reports, and optimize business processes based on the process mining results and process optimization reports.

[0050] Among them, process mining is a relatively new application of data mining in the field of business process management. Its principle is to extract the time and related information of each business process activity from the process event log recorded by the information system, so as to restore the actual working situation of the process. Process mining technology can investigate processes across the entire enterprise on a large scale and with low labor costs, analyze processes comprehensively and accurately based on facts, identify bottlenecks, deviations and inefficient processes that should be rethought or automated, continuously monitor processes and evaluate improvements.

[0051] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0052] Figure 1 Schematic diagram of the process optimization method provided for this application Figure 1 ,like Figure 1 As shown, the method includes:

[0053] S101. Obtain process log data of the business process to be optimized.

[0054] The process log data is a record generated in the information system when a business process event occurs. The process log data content includes at least the business serial number, operation node, operation time, operator and data content. The data content can be a record of the result of the process event. For example, if the process is a business approval process, the data content can be information such as "approved", "approved not", "returned", etc.

[0055] In one implementation, after the process log data is obtained from the information system, the process log data is further processed. The specific processing process is described in the following embodiments.

[0056] S102: Determine the core fields required for process mining corresponding to the business process to be optimized, and the monitoring indicators corresponding to the business process to be optimized.

[0057] In one implementation, based on the principle of process mining, the time and related information of each business process activity are extracted from the process log data recorded in the information system to restore the actual working situation of the process. The core fields must include the three elements of business serial number, operation node, and operation time. Based on the data corresponding to these three elements in the process log data, the process mining technology can be used to extract the flow of different business processes.

[0058] In addition, based on the three major elements of business serial number, operation node, and operation time, users can expand the core fields according to their focus. For example, if the user wants to pay attention to the processing efficiency of each node, the operation duration field can be added to the core field. During the process mining process, the operation duration of each node can be counted, and the time consumption of each process node can be reflected in the process mining results.

[0059] In one embodiment, the monitoring indicators corresponding to the business process to be optimized refer to setting observation items and their corresponding thresholds for the process mining results. When the observation items in the process mining results do not meet their corresponding thresholds, they are highlighted when the process optimization report is subsequently output. Exemplarily, the monitoring indicators are set to include node processing time and standard duration. During the process mining process, the node operation time field in the process log data is read and it is determined whether it exceeds the standard duration. The nodes that exceed the standard duration are recorded and reported when the subsequent process optimization report is output.

[0060] S103. Apply the process mining model to perform process mining on the process log data based on the core fields to obtain the process mining results for the business process to be optimized. The process mining model is used to mine the process path and optimize the process path.

[0061] In one embodiment, after the user inputs the core fields and monitoring indicators, the process mining model performs process mining on the business indicated by the core fields based on the process log data to obtain the process mining results of the business. The process mining results at least include the flowchart or event log of the business process to be optimized.

[0062] In one embodiment, the process mining model uses an inductive mining algorithm architecture for process mining. The inductive mining algorithm is an algorithm based on a process tree. The core idea is to automatically generate a process model from an event log through inductive logic programming technology. The specific steps are as follows: clean and preprocess the event log to ensure the integrity and consistency of the data; use inductive logic programming technology to infer a directed graph model from the event log; and verify the accuracy and applicability of the model by comparing the actual event log with the generated model.

[0063] In one implementation, in addition to the inductive mining algorithm, a heuristic mining algorithm or other frameworks may also be used. This application does not limit the selection of the process mining algorithm.

[0064] A readable flowchart needs to meet the following standards: the direction of the edges should be as consistent as possible, the graph should be compact, the layout should be symmetrical, long edges should be avoided as much as possible, the bending of the edges should be reduced, the crossing of the lines should be reduced, and the nodes should be balanced. In the process mining results, parallel processes can display the process more intuitively and concisely than serial processes. In the mining process, since the process is mined based on the time when the process events in the process log data occur, it is relatively easy to identify the serial process. The process mining model provided in this application strengthens the identification of parallel processes, reduces unnecessary node dependencies, and displays the business process path as intuitively and concisely as possible in the process mining results. The specific process path optimization means are described in detail in the embodiments below.

[0065] S104. Based on the process mining results and monitoring indicators, a process optimization report of the business process to be optimized is obtained.

[0066] The process optimization report displays the business process path and the compliance status of each monitoring item, providing a reference for subsequent process optimization.

[0067] The process optimization method provided in the embodiment of the present application first obtains the process log data of the business process to be optimized, uses the process mining model to mine the business process from the process log data, strengthens the optimization of the business process path, makes the process mining results clearer, and forms a process optimization report in combination with monitoring indicators, providing a clear and intuitive reference for process optimization.

[0068] Figure 2 Schematic diagram of the process optimization method provided in the embodiment of the present application Figure 2 In one embodiment, Figure 2 As shown in the figure, the process mining model is used to perform the following steps:

[0069] S201 , filtering data that does not conform to business logic in process log data to obtain first data that conforms to business logic.

[0070] In one embodiment, Redis cache technology is used to improve the platform response speed and process the process log data in batches. After the process log data is obtained in batches, the uncommon behaviors contained in the data are identified and filtered. For example, if the frequency of the output edge of a node is less than k times the frequency of the strongest output edge of the node, the edge with a lower frequency is determined to be an uncommon behavior, and the corresponding process log data is regarded as data that does not conform to the business logic and is removed from the batch data.

[0071] By eliminating data that does not conform to business logic, the clarity and accuracy of process mining results can be ensured.

[0072] S202: Analyze the first data using a stream processing technology to obtain a directed graph corresponding to the first data, where the directed graph includes a process path.

[0073] Specifically, stream processing technology (kafka) is applied to process and analyze the first data in real time. At the same time, columnar storage is used to optimize data reading performance, accelerate query analysis, and use load balancing to ensure efficient use of computing resources. Combined with the process mining algorithm, a directed graph corresponding to the first data is obtained, and the directed graph contains the process path.

[0074] S203, applying a visual layout algorithm to remove loops in the directed graph, assigning hierarchies to the nodes in the directed graph to identify parallel paths, and sorting the internal nodes of each level, designing the coordinates of each sorted node, and obtaining process mining results.

[0075] In one implementation, the above process is implemented using the Sugiyama Framework algorithm, which is a visual layout algorithm for graphically representing the relationship between data, and is intended to optimize the layout of a hierarchical graph to reduce intersections in the graph.

[0076] A readable flowchart needs to meet the following standards: the direction of the edges should be as consistent as possible, the graph should be compact, the layout should be symmetrical, long edges should be avoided as much as possible, the bending of the edges should be reduced, the crossing of the lines should be reduced, and the nodes should be balanced. This application uses the Sugiyama Framework to optimize the directed graph and obtain a flowchart that meets the above standards. The Sugiyama Framework consists of three steps: level allocation, node rearrangement, and coordinate specification.

[0077] Specifically, hierarchical allocation refers to placing flowchart nodes into designated layers. Currently, hierarchical allocation algorithms include the longest path algorithm, the minimum width algorithm, the virtual node minimum algorithm, etc. This application does not limit the selection of hierarchical allocation algorithms.

[0078] The number of edge crossings depends on the order of nodes in each layer. The number of edge crossings can be reduced by rearranging the nodes in the layer after the layer allocation is completed. The crossing reduction algorithm for the entire graph is called the K-layer crossing reduction algorithm, which can be decomposed into a 2-layer crossing reduction algorithm. Layer-by-layer-sweep is a general method for cyclically applying a 2-layer crossing reduction algorithm to reduce the number of edge crossings in the entire graph. Currently, the commonly used 2-layer crossing algorithms mainly include branch and cut heuristics, median heuristics, greedy-insert heuristics, greedy-switch heuristics, permutation heuristics, and barycenter heuristics. This application does not limit the selection of 2-layer crossing algorithms.

[0079] Coordinate specification mainly completes the coordinate calculation of all nodes and virtual nodes, while trying to reduce the excessive bending points caused by virtual nodes, so that the graphic layout is as compact as possible, and the cross-layer edges should be straightened as much as possible, thereby ensuring the readability and aesthetics of the graphics. The calculation of the y coordinate of the node is relatively simple. The y coordinates of the same layer are equal, and can be calculated by the shortest distance between layers and the height of the node itself. The calculation of the x coordinate of the node is the core of this step. It is necessary to reduce virtual nodes as much as possible. Commonly used algorithms include the Promote Layering algorithm, the routing algorithm, and the LP (Linear Program) algorithm. Among them, the Promote Layering algorithm reduces the number of virtual nodes by promoting the node layer; the basic idea of ​​the routing algorithm is to bypass the obstacles between the graphics and select a shortest path through the broken line to connect the nodes; the LP algorithm ensures that the cross-layer edges are straightened as much as possible and the nodes are as compact as possible, thereby achieving the readability and aesthetics of the graphic layout. This application does not limit the algorithm for determining the x coordinate.

[0080] The process optimization method provided in the embodiment of the present application batch reads and processes process log data, identifies and filters process data that does not conform to business logic, improves the clarity and accuracy of process mining results, uses stream processing technology combined with process mining algorithms to efficiently obtain a directed graph containing process paths, and uses the Sugiyama Framework algorithm on the basis of the directed graph to enhance the optimization of process display, and finally obtains a clear and concise flow chart.

[0081] In one embodiment, the structure of the process mining model is as follows: Figure 3As shown in the figure, the process mining model adopts a modular design, which is easy to expand and reuse during use.

[0082] Specifically, the process mining model sets a standardized interface. The standardized interface achieves standardization from two aspects: first, parameter standardization, using the three major elements of process mining described in the above embodiment, namely, business serial number, operation node, and operation time as standard parameters to ensure the smooth progress of process mining; second, data reading standardization, the process mining model has built-in multiple data source adaptation principles, which can adapt to multiple data sources such as XML, database, API, etc. After reading data from these data sources, the data is standardized before subsequent process mining.

[0083] The process mining model sets the parameter configuration interface, and the user can configure the parameters of the process mining model according to his own needs. For example, in the core fields required for process mining corresponding to the optimized business process determined in step S102 of the above embodiment, in addition to the three major elements required for process mining, the user can also expand some fields according to his own needs, and the user's expanded fields are input into the process mining model through the parameter configuration interface. In addition, the user can also set the selection of the process mining model algorithm through the parameter configuration interface.

[0084] The process mining model has a process mining algorithm library, which has built-in multiple optional process mining algorithms and statistical algorithms mentioned in the above embodiments, and is automatically called during the process mining process.

[0085] The process mining model provided in the embodiment of the present application uses a modular design concept, sets a standardized interface, a parameter configuration interface and a process mining algorithm library, so as to achieve adaptation to multiple data sources, expandable functions and switchable process mining algorithms.

[0086] In one implementation, after obtaining the process mining results for the business process to be optimized, the process mining results are displayed based on a set display type.

[0087] Specifically, the visualization component is used to display the mining results based on the set display type. The visualization component can be embedded in the process mining model, and the process mining directly outputs the visualized process mining results. The visualization component can also be provided by the process optimization system platform, and the process mining model outputs the process mining results to the visualization component for visualization processing.

[0088] The visualization component provides a variety of statistical charts such as flow charts, bar charts, pie charts, and line charts, as well as a variety of flexible ways to display processes and process analysis results, further enhancing its intuitiveness and readability.

[0089] The process optimization method provided in the embodiment of the present application visualizes the process mining results, intuitively presents the process mining results, enhances the readability of the process mining results, and provides a convenient reference for subsequent process optimization based on the process mining results.

[0090] In one embodiment, the process optimization method further includes: optimizing the business process to be optimized using artificial intelligence technology based on the process optimization report.

[0091] After obtaining the process optimization report, users can intuitively understand the current business process flow, including the bottlenecks and difficulties of the process. Artificial intelligence technology is artificially introduced to optimize the inefficient nodes in the process and improve its efficiency.

[0092] The introduction of artificial intelligence technology can significantly optimize cumbersome business links and improve efficiency and quality. For example, the introduction of natural language processing, robotic process automation, optical character recognition, artificial intelligence operation and maintenance, knowledge base, etc. can achieve intelligent optimization of cumbersome business links and improve efficiency and quality. Using natural language processing to build a variety of large model applications, such as large model dialogue assistants, large model knowledge bases, and large model credit assistants, can improve the efficiency of interacting with customers in business; robotic process automation can perform highly repetitive and rule-clear tasks, such as data entry and report generation, reduce manual operations, and significantly improve work efficiency; optical character recognition technology can convert paper documents into editable digital texts and automatically extract key information, thereby reducing manual input errors and time consumption; intelligent operation and maintenance monitors system performance through machine learning, automatically detects and repairs problems, predicts potential failures, and improves system stability and reliability.

[0093] The process optimization method provided in this application introduces artificial intelligence technology to optimize inefficient nodes in the process and improve the efficiency of process flow. Through the comprehensive application of various artificial intelligence technologies, it not only realizes the intelligent transformation of business processes, but also enhances customer satisfaction and improves the overall service quality, providing strong support for the digital transformation of enterprises.

[0094] Figure 4 Schematic diagram of the process optimization method provided in the embodiment of the present application Figure 3 In one embodiment, Figure 4 As shown, the acquisition of process log data includes the following steps:

[0095] S401. Collect all relevant log data from the business system of the business process to be optimized.

[0096] In the process of business process flow, process log data is usually generated on each business node, which will be integrated, recorded and archived in the business system. Reading this data and further processing it can obtain the raw materials for process mining.

[0097] S402: pre-process the collected log data by cleaning, desensitizing, and integrating to obtain process log data of the business process to be optimized, and form a process data mart.

[0098] First, data cleaning includes checking data quality, handling missing values, and standardizing data formats. Secondly, data desensitization is performed to encrypt or obfuscate sensitive information (such as customer names) to protect customer privacy. Finally, data integration involves merging data from different sources, ensuring field consistency, and connecting related tables by matching primary keys to form a complete data mart that includes business process tables and business indicator sets. The data mart contains business serial numbers, business process node information, etc., providing reliable basic data for process mining.

[0099] In one implementation, the step S101 of acquiring the process log data of the business process to be optimized in the above embodiment includes: acquiring the process log data of the business process to be optimized from a process data mart.

[0100] The process optimization method provided in the embodiment of the present application obtains original log data from the business system, processes the original log data, that is, cleans, desensitizes, and integrates the original log data to form a data mart with a unified data format and can be read efficiently, thereby providing reliable data support for subsequent process mining.

[0101] Figure 5 The schematic diagram of the process optimization device provided in this application is as follows: Figure 5 As shown, the process optimization device 50 provided in this embodiment includes:

[0102] The acquisition module 501 is used to acquire the process log data of the business process to be optimized.

[0103] The determination module 502 is used to determine the core fields required for process mining corresponding to the business process to be optimized, and the monitoring indicators corresponding to the business process to be optimized.

[0104] Processing module 503 is used to apply a process mining model to perform process mining on the process log data based on the core fields to obtain process mining results for the business process to be optimized, wherein the process mining model is used to mine process paths and identify parallel paths in the process paths; and, based on the process mining results and the monitoring indicators, obtain a business process optimization report for the business process to be optimized.

[0105] In a possible implementation, the processing module 503 is further configured to filter data in the process log data that does not conform to the business logic, and obtain first data that conforms to the business logic.

[0106] The acquisition module 501 is further used to apply stream processing technology to analyze the first data to obtain a directed graph corresponding to the first data, where the directed graph includes a process path.

[0107] The processing module 503 is also used to apply a visual layout algorithm to remove loops in the directed graph, assign levels to the nodes in the directed graph to identify parallel paths, sort the internal nodes of each level, design the coordinates of each sorted node, and obtain process mining results.

[0108] In a possible implementation manner, the processing module 503 is further configured to:

[0109] After obtaining the process mining results for the business process to be optimized, the process mining results are displayed based on the set display type.

[0110] In a possible implementation manner, the processing module 503 is further configured to:

[0111] Based on the process optimization report, use artificial intelligence technology to optimize the business processes to be optimized.

[0112] In a possible implementation, the processing module 503 is further configured to collect all relevant log data from the business system of the business process to be optimized.

[0113] The processing module 503 is also used to perform pre-processing of cleaning, desensitization and integration on the collected log data to obtain the process log data of the business process to be optimized and form a process data mart;

[0114] The acquisition module 501 is used to acquire the process log data of the business process to be optimized, including: acquiring the process log data of the business process to be optimized from the process data mart.

[0115] The process optimization device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0116] Figure 6 This is a schematic diagram of the structure of the process optimization equipment provided in this application. Figure 6 As shown, the electronic device 60 provided in this embodiment includes: at least one processor 601 and a memory 602. Optionally, the device 60 also includes a communication component 603. The processor 601, the memory 602 and the communication component 603 are connected via a bus 604.

[0117] In a specific implementation process, at least one processor 601 executes the computer execution instructions stored in the memory 602, so that at least one processor 601 executes the above method.

[0118] The specific implementation process of the processor 601 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0119] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0120] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0121] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0122] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0123] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0124] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0125] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0126] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0129] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0130] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0131] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A process optimization method, characterized in that: include: Obtain process log data of the business process to be optimized; Determine the core fields required for process mining corresponding to the business process to be optimized, and the monitoring indicators corresponding to the business process to be optimized; Applying a process mining model, based on the core fields, to perform process mining on the process log data, to obtain a process mining result for the business process to be optimized, wherein the process mining model is used to mine a process path and optimize the process path; Based on the process mining results and the monitoring indicators, a process optimization report of the business process to be optimized is obtained.

2. The process optimization method according to claim 1, characterized in that: The process mining model is specifically used for: Filter the data in the process log data that does not conform to the business logic to obtain the first data that conforms to the business logic; Applying a stream processing technology to analyze the first data, obtaining a directed graph corresponding to the first data, wherein the directed graph includes a process path; A visual layout algorithm is applied to remove the loop in the directed graph, the nodes in the directed graph are allocated to levels to identify parallel paths, and the internal nodes of each level are sorted, and the coordinates of each sorted node are designed to obtain the process mining result.

3. The process optimization method according to claim 1, characterized in that: The process mining model also has at least one of the following functions: Modular design; A standardized interface is provided; There are multiple process mining algorithms built in; A parameter configuration interface is provided to configure parameters of the process mining model through the parameter configuration interface.

4. The method according to any one of claims 1 to 3, characterized in that The process optimization also includes: After obtaining the process mining result for the business process to be optimized, the process mining result is displayed based on the set display type.

5. The process optimization method according to any one of claims 1 to 3, characterized in that: The process optimization method further includes: According to the process optimization report, artificial intelligence technology is used to optimize the business process to be optimized.

6. The process optimization method according to any one of claims 1 to 3, characterized in that: The process log data is obtained in the following way: Collect all relevant log data from the business system of the business process to be optimized; The collected log data is pre-processed by cleaning, desensitizing and integrating to obtain the process log data of the business process to be optimized and form a process data mart; The obtaining of the process log data of the business process to be optimized includes: obtaining the process log data of the business process to be optimized from the process data mart.

7. A process optimization device, characterized in that: include: An acquisition module is used to obtain process log data of the business process to be optimized; A determination module, used to determine the core fields required for process mining corresponding to the business process to be optimized, and the monitoring indicators corresponding to the business process to be optimized; A processing module, used for applying a process mining model to perform process mining on the process log data based on the core fields to obtain a process mining result for the business process to be optimized, wherein the process mining model is used to mine process paths and identify parallel paths in the process paths; And, based on the process mining results and the monitoring indicators, a business process optimization report of the business process to be optimized is obtained.

8. A process optimization device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed.

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