Business process mining method, device and equipment and readable storage medium

Abstract

A business process mining method, apparatus and device, and a readable storage medium, the business process mining method comprising: acquiring each node of a business process and a collection field of each node, the collection field comprising a node field and a business field; aiming at each node of the business process, collecting an event log generated by executing the node in the execution process of the business process by setting a buried point, and obtaining corresponding node data and business data from the event log according to a node field and a business field of the node to obtain node data and business data of each node; and constructing a business process view based on the node data of each node, and analyzing the business process based on the node data and the business data of each node. Through the method and the device, complex work of extracting event data from huge and disordered event logs and carrying out a series of processing on the event data in traditional process mining is avoided, and the efficiency of business process mining is improved.

Description

Technical Field

[0001] The present application relates to the field of process mining technology, and in particular to a business process mining method, device, equipment and readable storage medium. Background Art

[0002] Process mining, also known as workflow mining, is a technology that extracts useful information from workflow logs. For example, it can find workflow models and organizational models from the workflow logs of an ERP (Enterprise Resource Planning) system, and then analyze them to find problems in the process.

[0003] Traditional process mining first records a large number of event logs, and then transforms these non-standardized event logs into standard event data that can be used for process mining through data governance. Then, through process mining tools or implementation of relevant process mining algorithms, the process is constructed and process information is mined based on the event data.

[0004] However, the amount of event log data in traditional process mining is huge and messy, which makes data governance difficult. In addition, the field content of the event data obtained after data governance is inconsistent, the structure is not uniform, and there is too much dirty data, which requires a lot of time for data cleaning, data structuring, and data labeling. In summary, the efficiency of traditional process mining is low. Summary of the invention

[0005] The present application provides a business process mining method, apparatus, device and readable storage medium, aiming to solve the technical problem of low efficiency of traditional process mining.

[0006] In a first aspect, an embodiment of the present application provides a business process mining method, the business process mining method comprising:

[0007] Acquire each node of the business process and the collection field of each node, wherein the collection field includes a node field and a business field;

[0008] For each node of the business process, by setting up tracking points to collect the event logs generated by the execution nodes during the execution of the business process, the corresponding node data and business data are obtained from the event logs according to the node fields and business fields of the nodes, and the node data and business data of each node are obtained;

[0009] A business process view is constructed based on the node data of each node, and the business process is analyzed based on the node data and business data of each node.

[0010] Optionally, the analyzing the business process based on the node data and business data of each node includes:

[0011] Count the number of occurrences of each node;

[0012] For each node, when the number of occurrences of the node is higher than the preset maximum number of the node or lower than the preset minimum number of the node, the business data of the node is output to analyze the cause of the node's deviation based on the business data of the node.

[0013] Optionally, the node includes a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the analyzing of the business process based on the node data and business data of each node further includes:

[0014] Count the paths formed by each node and count the number of occurrences of each path;

[0015] For each path, when the number of occurrences of the path is higher than the preset maximum number of the path or lower than the preset minimum number of the path, the business data of each node on the path is output to analyze the cause of the path deviation based on the business data of each node on the path.

[0016] Optionally, the node includes a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the analyzing of the business process based on the node data and business data of each node further includes:

[0017] Count the paths formed by each node, and count and output the proportion of each path that does not reach the terminal node to all paths;

[0018] For each path that does not reach the terminal node, the business data of each node on the path is output, so as to analyze the cause of the path deviation according to the business data of each node on the path.

[0019] Optionally, the analyzing the business process based on the node data and business data of each node further includes:

[0020] Count the time spent on each node;

[0021] For each node, when the time consumed by the node exceeds the preset time consumed by the node, the business data of the node is output to analyze the blocking cause of the node according to the business data of the node.

[0022] Optionally, the analyzing the business process based on the node data and business data of each node further includes:

[0023] The total time consumed for each execution of the business process, the total number of nodes that appear, the loop path, and the average time consumed for multiple executions of the business process are counted and output to analyze the complexity of the business process.

[0024] Optionally, the analyzing the business process based on the node data and business data of each node further includes:

[0025] Count the successor nodes of each node;

[0026] For each node, when the node has multiple successor nodes, the business data of each successor node is output so as to analyze the user's decision tendency based on the business data of each successor node.

[0027] In a second aspect, an embodiment of the present application provides a business process mining device, the business process mining device comprising:

[0028] An acquisition module, used to acquire each node of a business process and a collection field of each node, wherein the collection field includes a node field and a business field;

[0029] The data collection module is used to collect event logs generated by execution nodes during the execution of business processes by setting tracking points for each node of the business process, and obtain corresponding node data and business data from the event log according to the node field and business field of the node, so as to obtain the node data and business data of each node;

[0030] An analysis module is constructed to construct a business process view based on the node data of each node, and to analyze the business process based on the node data and business data of each node.

[0031] Optionally, the node field includes a process number, an event name, and a timestamp, and the construction analysis module is further used to:

[0032] Based on the process number and event name of each node, the business process view is constructed in the order of timestamps.

[0033] In a third aspect, an embodiment of the present application provides a business process mining device, which includes a processor, a memory, and a business process mining program stored on the memory and executable by the processor, wherein when the business process mining program is executed by the processor, the steps of the business process mining method described above are implemented.

[0034] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a business process mining program is stored, wherein when the business process mining program is executed by a processor, the steps of the business process mining method as described above are implemented.

[0035] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0036] In an embodiment of the present application, each node of a business process and the collection fields of each node are obtained, and the collection fields include node fields and business fields; for each node of the business process, the event log generated by the execution node during the execution of the business process is collected by setting a buried point, and the corresponding node data and business data are obtained from the event log according to the node field and business field of the node, so as to obtain the node data and business data of each node; a business process view is constructed based on the node data of each node, and the business process is analyzed based on the node data and business data of each node. The embodiment of the present application first determines each node and the collection fields of each node contained in a business process to be mined, sets a buried point at each node, and obtains the node data and business data of each node from the event log according to the node field and business field, and then constructs the business process view and analyzes the business process, thereby avoiding the complex work of traditional process mining in extracting event data from large and messy event logs and performing a series of processing on the event data, and improving the efficiency of business process mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flowchart of an embodiment of the business process mining method of the present application;

[0038] Figure 2 A schematic diagram of the system structure of an embodiment of the business process mining method of the present application;

[0039] Figure 3 This is a functional module diagram of an embodiment of a business process mining device of the present application;

[0040] Figure 4 This is a schematic diagram of the hardware structure of the business process mining device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0043] In a first aspect, an embodiment of the present application provides a business process mining method.

[0044] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the business process mining method of the present application. Figure 1 As shown, business process mining methods include:

[0045] Step S10, acquiring each node of the business process and the collection field of each node, wherein the collection field includes a node field and a business field.

[0046] In this embodiment, it is first necessary to understand that process mining is a technology for analyzing and monitoring processes. Process mining can be applied to various industries, such as financial services, telecommunications, manufacturing, healthcare, consumer products, and e-commerce. It can analyze various business processes in various industries, such as contact centers, purchase-to-payment, and order-to-collection. Taking a business process for project subsidy application as an example, it is first necessary to obtain each node and the collection field of each node included in the project subsidy application business process. Each node is, for example, attachment upload, attachment deletion, form verification, project submission, project approval, and project completion. The collection field of each node can be divided into node field and business field. The node field includes process number, event name, operator, and timestamp, etc. The business field can set different business fields according to the specific node. Specifically, for the attachment upload node, its business field may include attachment type and attachment size, etc.

[0047] Step S20, for each node of the business process, by setting up tracking points to collect the event logs generated by the execution nodes during the execution of the business process, according to the node fields and business fields of the nodes, the corresponding node data and business data are obtained from the event logs to obtain the node data and business data of each node.

[0048] In this embodiment, continuing to take the business process of project subsidy application as an example, a tracking point can be set at each node of attachment upload, attachment deletion, form verification, project submission, project approval and project completion, etc. The event log generated by executing each node during the execution of the project subsidy application business process is collected through the set tracking points, and the corresponding node data and business data are obtained from the event log according to the node field and business field of each node, so as to obtain the node data and business data of each node. Among them, setting a tracking point is a commonly used data collection method. For the "node" that needs to collect data, the program code for data collection is attached to its functional program code, and the user behavior or event when the "node" is executed is captured, and the captured user behavior or event is sent to the relevant data processing device. The event log can be diverse, such as database, text file, message log, transaction log, ERP system and file system.

[0049] Step S30, constructing a business process view based on the node data of each node, and analyzing the business process based on the node data and business data of each node.

[0050] In this embodiment, the node data corresponding to the node field is the basis for constructing the business process. The node field may include the process number, event name, operator and timestamp, etc. The business process view can be constructed based on the process number and event name in the node data of each node in the order of timestamps. Among them, the process number is the unique number of the node assigned by the system when the business process is executed. The event name corresponds to the key operation of the user, not all the operations of the user. Taking the online shopping system as an example, the user placing an order is the key operation in the order purchase process and needs to be recorded. The user entering personal information and order address is a non-key operation and does not need to be recorded. The timestamp records the time when the operation occurs, which is the basis for judging the order of the nodes. Specifically, it can be implemented and expanded based on the open source Pm4py library, or using existing process mining tools such as ProM, etc., based on the node data of each node such as process number, event name, operator and timestamp, etc., to build a visual business process view, and analyze the business process based on the node data and business data of each node, such as process deviation analysis, blocking analysis, process complexity analysis and user decision tendency analysis. Among them, the business data reflects the detailed business characteristics of the node, which can provide effective help for the mining analysis and subsequent optimization of the business process.

[0051] In this embodiment, refer to Figure 2 , Figure 2 This is a schematic diagram of the system structure of an embodiment of the business process mining method of this application. Figure 2As shown, the business process mining system corresponding to this method can be divided into three layers from the software level: point setting and data collection layer 1001, business process construction layer 1002 and business process mining layer 1003. Among them, the point setting and data collection layer 1001 can not only be used as a data source for data analysis and various dashboards, but also as a data source for the business process construction layer 1002. The point setting and data collection layer 1001 sets points at key operation nodes according to different business process design requirements, only collects operation log data related to the business process, excludes some meaningless operation logs of users, and pushes the obtained data to the data warehouse for storage at regular intervals to reduce the storage pressure of the business server, and the collected fields have a unified format, such as process number, event name, operator and timestamp, etc., and extended business fields can also be added according to specific needs. Structured node data and business data are collected through the setting and collection of the early point, which simplifies and reduces the difficulty of constructing event data in process mining. Subsequently, the process mining algorithm is implemented through process mining tools or self-developed process mining components to complete the analysis of business processes and assist in completing process optimization. The business process construction layer 1002 discovers and visualizes the business process based on the node data collected by the tracking point settings and the data collection layer 1001. The business process construction layer 1002 can have data of multiple business processes. The data of the same business process can come from the same table, and the node data of different business processes are stored in different tables. The business process construction layer 1002 can access existing process mining tools, such as Pm4py or ProM, or can be independently developed according to the process discovery algorithm. It has high repeatability and can meet different business and scenario requirements without relying on fixed business process design. The business process mining layer 1003 takes the mining tool of the business process construction layer 1002 as the core, assisted by data statistics and ranking, and is used to analyze abnormal conditions in the process, such as process deviation, ping-pong effect, user decision preferences and process blocking nodes, and ultimately achieves the purpose of assisting in optimizing the business process system. By setting standardized collection fields and obtaining structured node data and business data through point collection, the complex work of traditional process mining, which involves extracting event data from large and messy event logs and performing a series of processing on the event data, is solved. The efficiency of business process mining is improved. Based on the node data of each node, such as process number, event name, operator and timestamp, a visual business process view can be efficiently constructed. Based on the node data and business data of each node, the business process can be analyzed. The business data reflects the detailed business characteristics of the node, and can provide effective assistance for the mining and analysis of the business process and subsequent optimization.

[0052] Furthermore, in one embodiment, the analyzing of the business process based on the node data and business data of each node includes:

[0053] Count the number of occurrences of each node;

[0054] For each node, when the number of occurrences of the node is higher than the preset maximum number of the node or lower than the preset minimum number of the node, the business data of the node is output to analyze the cause of the node's deviation based on the business data of the node.

[0055] In this embodiment, the number of occurrences of each node of the business process that is executed multiple times within a certain preset time period can be counted. The number of occurrences of the node is the number of times the node is executed. For each node, the number of occurrences of the node is compared with the design status of the business process. When designing the business process, a maximum number of occurrences and a minimum number of occurrences of the node can be pre-set. If the number of occurrences of a node is found to be too high or too low compared with the number of occurrences, it means that the node has deviated from the design status, and the business data of the node is output accordingly. The business data reflects the detailed business characteristics of the node. Continuing with the business process of project subsidy declaration as an example, the business fields of the attachment upload node include attachment type and attachment size. According to the output attachment type and attachment size data, the cause of the node deviation can be effectively analyzed. For example, if the attachment size is found to be relatively large, it can be determined that the node deviation is caused by the large attachment size.

[0056] Furthermore, in one embodiment, the node includes a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the analyzing of the business process based on the node data and business data of each node also includes:

[0057] Count the paths formed by each node and count the number of occurrences of each path;

[0058] For each path, when the number of occurrences of the path is higher than the preset maximum number of the path or lower than the preset minimum number of the path, the business data of each node on the path is output to analyze the cause of the path deviation based on the business data of each node on the path.

[0059] In this embodiment, the nodes of a business process include a start node and an end node, and the design state of the business process is to execute from the start node to the end node. It should be noted that the execution of the business process from the start node to the end node can be completed through multiple paths. Therefore, the paths formed by each node are counted, and the number of occurrences of each path is counted. For each path, the number of occurrences of the path is also compared with the design state of the business process. The maximum number of occurrences and the minimum number of occurrences of each path can be pre-set when designing the business process. If the number of occurrences of a path is found to be too high or too low compared with the number of occurrences, it means that the path has deviated from the design state, and the business data of each node on the path is output accordingly, so that the cause of the deviation of the path can be analyzed based on the business data of each node on the path.

[0060] Furthermore, in one embodiment, the node includes a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the analyzing of the business process based on the node data and business data of each node also includes:

[0061] Count the paths formed by each node, and count and output the proportion of each path that does not reach the terminal node to all paths;

[0062] For each path that does not reach the terminal node, the business data of each node on the path is output, so as to analyze the cause of the path deviation according to the business data of each node on the path.

[0063] In this embodiment, the nodes of the same business process include a start node and an end node. The design state of the business process is to execute from the start node to the end node. The execution of the business process from the start node to the end node can be completed through multiple paths. First, the paths formed by each node are counted. If it is detected that a path has not reached the end node within a certain statistical preset time, it means that the path has deviated from the design state. The proportion of each path that has not reached the end node to all paths is counted and output, and for each path that has not reached the end node, the business data of each node on the path is output, so as to analyze the cause of the deviation of the path based on the business data of each node on the path.

[0064] Furthermore, in one embodiment, the analyzing of the business process based on the node data and business data of each node also includes:

[0065] Count the time spent on each node;

[0066] For each node, when the time consumed by the node exceeds the preset time consumed by the node, the business data of the node is output to analyze the blocking cause of the node according to the business data of the node.

[0067] In this embodiment, the average duration or longest duration of each node in which the business process is executed multiple times within a certain preset time period can be counted. Specifically, the difference between the timestamp of leaving the node and the timestamp of entering the node in the node data can be used as the node's duration. If the duration of a node exceeds the preset duration of the node, it means that the node is blocked. The business data of the node is further output to analyze the cause of the node's blockage based on the node's business data. If the delay is caused by human operation, the human resource scheduling is optimized. If the system fails to notify the individual operator in time, the system notification module is optimized to optimize and improve the efficiency of the business process.

[0068] Furthermore, in one embodiment, the analyzing of the business process based on the node data and business data of each node also includes:

[0069] The total time consumed for each execution of the business process, the total number of nodes that appear, the loop path, and the average time consumed for multiple executions of the business process are counted and output to analyze the complexity of the business process.

[0070] In this embodiment, the total time consumed for each execution of the business process and the average time consumed for multiple executions of the business process are counted and output. The above two indicators can reflect the complexity of the business process to a certain extent. If the total time consumed for each execution of the business process and the average time consumed for multiple executions of the business process are too high, it means that the complexity of the business process is relatively high. A loop path refers to a loop path formed by multiple executions of two or more nodes back and forth. The loop path also reflects the complexity of the business process to a certain extent. The total number of nodes that appear can be compared with the design status of the business process. If the total number of nodes that appear is too high, it can also reflect to a certain extent that the complexity of the business process is relatively high.

[0071] Furthermore, in one embodiment, the analyzing of the business process based on the node data and business data of each node also includes:

[0072] Count the successor nodes of each node;

[0073] For each node, when the node has multiple successor nodes, the business data of each successor node is output so as to analyze the user's decision tendency based on the business data of each successor node.

[0074] In this embodiment, a purchase business process in e-commerce is specifically taken as an example. For example, the purchase node of a certain product includes two successor nodes, and the business data of the two successor nodes are 500kg and 1000kg respectively, which means that the user can purchase the product in two specifications of 500kg or 1000kg. By outputting the business data of the two subsequent nodes, the user's decision-making tendency can be analyzed. For example, if the user needs to purchase 5000kg of goods, he is more inclined to order 10 500kg rather than 5 1000kg goods. The single-portion product can be optimized to 100kg and 500kg based on this data, thereby providing the user with a more accurate product weight selection. For example, after the adjustment, more small orders may appear, further increasing the total sales volume of the product and realizing an increase in business profits.

[0075] In a second aspect, an embodiment of the present application also provides a business process mining device.

[0076] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of a business process mining device of the present application. Figure 3 As shown, the business process mining device includes:

[0077] An acquisition module 10 is used to acquire each node of a business process and a collection field of each node, wherein the collection field includes a node field and a business field;

[0078] The data collection module 20 is used to collect event logs generated by executing nodes during the execution of business processes by setting embedding points for each node of the business process, and obtain corresponding node data and business data from the event log according to the node field and business field of the node to obtain the node data and business data of each node;

[0079] The construction and analysis module 30 is used to construct a business process view based on the node data of each node, and analyze the business process based on the node data and business data of each node.

[0080] Furthermore, in one embodiment, the analysis module 30 is constructed to:

[0081] Count the number of occurrences of each node;

[0082] For each node, when the number of occurrences of the node is higher than the preset maximum number of the node or lower than the preset minimum number of the node, the business data of the node is output to analyze the cause of the node's deviation based on the business data of the node.

[0083] Further, in one embodiment, the node includes a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the construction analysis module 30 is also used to:

[0084] Count the paths formed by each node and count the number of occurrences of each path;

[0085] For each path, when the number of occurrences of the path is higher than the preset maximum number of the path or lower than the preset minimum number of the path, the business data of each node on the path is output to analyze the cause of the path deviation based on the business data of each node on the path.

[0086] Further, in one embodiment, the node includes a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the construction analysis module 30 is also used to:

[0087] Count the paths formed by each node, and count and output the proportion of each path that does not reach the terminal node to all paths;

[0088] For each path that does not reach the terminal node, the business data of each node on the path is output, so as to analyze the cause of the path deviation according to the business data of each node on the path.

[0089] Furthermore, in one embodiment, the analysis module 30 is constructed to:

[0090] Count the time spent on each node;

[0091] For each node, when the time consumed by the node exceeds the preset time consumed by the node, the business data of the node is output to analyze the blocking cause of the node according to the business data of the node.

[0092] Furthermore, in one embodiment, the analysis module 30 is constructed to:

[0093] The total time consumed for each execution of the business process, the total number of nodes that appear, the loop path, and the average time consumed for multiple executions of the business process are counted and output to analyze the complexity of the business process.

[0094] Furthermore, in one embodiment, the analysis module 30 is constructed to:

[0095] Count the successor nodes of each node;

[0096] For each node, when the node has multiple successor nodes, the business data of each successor node is output so as to analyze the user's decision tendency based on the business data of each successor node.

[0097] Among them, the functional implementation of each module in the above-mentioned business process mining device corresponds to each step in the above-mentioned business process mining method embodiment, and its functions and implementation processes are no longer repeated here.

[0098] In a third aspect, an embodiment of the present application provides a business process mining device.

[0099] Reference Figure 4 , Figure 4 The hardware structure diagram of the business process mining device involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the business process mining device may include a processor, a memory, a communication interface, and a communication bus.

[0100] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0101] The communication interface includes input / output (I / O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the business process mining device, and the interface used to realize the interconnection between the business process mining device and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0102] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0103] The processor may be a general-purpose processor, and the general-purpose processor may call the business process mining program stored in the memory and execute the business process mining method provided by the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the business process mining program is called may refer to the various embodiments of the business process mining method of the present application, which will not be repeated here.

[0104] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0105] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0106] The readable storage medium of the present application stores a business process mining program, wherein when the business process mining program is executed by a processor, the steps of the above-mentioned business process mining method are implemented.

[0107] Among them, the method implemented when the business process mining program is executed can refer to the various embodiments of the business process mining method of the present application, and will not be repeated here.

[0108] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0109] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0110] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0111] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0112] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0114] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A business process mining method, characterized in that: The business process mining method comprises: Acquire each node of the business process and the collection field of each node, wherein the collection field includes a node field and a business field; For each node of the business process, by setting up tracking points to collect the event logs generated by the execution nodes during the execution of the business process, the corresponding node data and business data are obtained from the event logs according to the node fields and business fields of the nodes, and the node data and business data of each node are obtained; A business process view is constructed based on the node data of each node, and the business process is analyzed based on the node data and business data of each node.

2. The business process mining method according to claim 1, characterized in that: The analysis of the business process based on the node data and business data of each node includes: Count the number of occurrences of each node; For each node, when the number of occurrences of the node is higher than the preset maximum number of the node or lower than the preset minimum number of the node, the business data of the node is output to analyze the cause of the node's deviation based on the business data of the node.

3. The business process mining method according to claim 1, characterized in that: The nodes include a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the analysis of the business process based on the node data and business data of each node also includes: Count the paths formed by each node and count the number of occurrences of each path; For each path, when the number of occurrences of the path is higher than the preset maximum number of the path or lower than the preset minimum number of the path, the business data of each node on the path is output to analyze the cause of the path deviation based on the business data of each node on the path.

4. The business process mining method according to claim 1, characterized in that: The nodes include a start node and an end node, the design state of the business process is to execute from the start node to the end node, and the analysis of the business process based on the node data and business data of each node also includes: Count the paths formed by each node, and count and output the proportion of each path that does not reach the terminal node to all paths; For each path that does not reach the terminal node, the business data of each node on the path is output, so as to analyze the cause of the path deviation according to the business data of each node on the path.

5. The business process mining method according to claim 1, characterized in that: The analysis of the business process based on the node data and business data of each node also includes: Count the time spent on each node; For each node, when the time consumed by the node exceeds the preset time consumed by the node, the business data of the node is output to analyze the blocking cause of the node according to the business data of the node.

6. The business process mining method according to claim 1, characterized in that: The analysis of the business process based on the node data and business data of each node also includes: The total time consumed for each execution of the business process, the total number of nodes that appear, the loop path, and the average time consumed for multiple executions of the business process are counted and output to analyze the complexity of the business process.

7. The business process mining method according to claim 1, characterized in that: The analysis of the business process based on the node data and business data of each node also includes: Count the successor nodes of each node; For each node, when the node has multiple successor nodes, the business data of each successor node is output so as to analyze the user's decision tendency based on the business data of each successor node.

8. A business process mining device, characterized in that: The business process mining device comprises: An acquisition module, used to acquire each node of a business process and a collection field of each node, wherein the collection field includes a node field and a business field; The data collection module is used to collect event logs generated by execution nodes during the execution of business processes by setting tracking points for each node of the business process, and obtain corresponding node data and business data from the event log according to the node field and business field of the node, so as to obtain the node data and business data of each node; An analysis module is constructed to construct a business process view based on the node data of each node, and to analyze the business process based on the node data and business data of each node.

9. A business process mining device, characterized in that: The business process mining device includes a processor, a memory, and a business process mining program stored in the memory and executable by the processor, wherein when the business process mining program is executed by the processor, the steps of the business process mining method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a business process mining program, wherein when the business process mining program is executed by a processor, the steps of the business process mining method according to any one of claims 1 to 7 are implemented.

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