Business process optimization method, device, terminal equipment and storage medium

Abstract

The present invention discloses a method, device, terminal device and computer-readable storage medium for optimizing a business process, the method comprising: determining a set of activity nodes corresponding to the business process; identifying abnormal activities in the set of activity nodes to generate an abnormal activity set; calculating the effectiveness of abnormal activity identification according to the abnormal activity set; building a preliminary process model and evaluating the effectiveness of abnormal activity identification based on the preliminary process model to optimize the business process. The present invention can discover a more accurate process model from an event log, and provide decision support for process optimization, effectively improving the efficiency of optimizing the business process.

Description

Technical Field

[0001] The present invention relates to the technical field of business process processing, and in particular to a business process optimization method, device, terminal equipment and computer-readable storage medium. Background Art

[0002] Nowadays, with the continuous operation of the system for business process management, the accumulated business process logs and other data are becoming more and more. Therefore, it is urgent to analyze and utilize the log data. The main way to analyze and utilize log data is to extract process knowledge from the log to build a process model to detect and assist the enterprise process and improve the enterprise process.

[0003] Since event logs in real life often contain some abnormal activities that are independent of the process and may occur at any time, and these abnormal activities will have a serious impact on the quality of the process model used to identify in process mining, such abnormal activities have to be filtered out in the log data. The existing operation of filtering abnormal activities is to simply filter out infrequent activities as abnormal activities based on frequency. However, since infrequent activities may also be important activity indicators or new activity nodes added in the early stage of the optimization process, simply filtering infrequent activities directly may accidentally delete valuable activities. Summary of the invention

[0004] The main purpose of the present invention is to provide a business process optimization method, apparatus, terminal device and computer-readable storage medium, aiming to determine the effectiveness of abnormal activity filtering from an efficiency perspective based on data envelopment analysis, thereby realizing the discovery of more accurate process models from event logs to provide decision support for process optimization.

[0005] To achieve the above object, the present invention provides a method for optimizing a business process, the method comprising:

[0006] Determine the set of activity nodes corresponding to the business process;

[0007] identifying abnormal activities in the set of activity nodes to generate an abnormal activity set;

[0008] Calculating the abnormal activity recognition effectiveness according to the abnormal activity set;

[0009] A preliminary process model is constructed and the effectiveness of the abnormal activity identification is evaluated based on the preliminary process model to optimize the business process.

[0010] Furthermore, the step of constructing a preliminary process model and evaluating the effectiveness of identifying abnormal activities based on the preliminary process model to optimize the business process includes:

[0011] constructing a preliminary process model based on trajectory information and activity nodes extracted from event log data corresponding to the business process;

[0012] The effectiveness of identifying abnormal activities is evaluated based on the preliminary process model to perform step-by-step filtering of abnormal activities, so as to iteratively update the preliminary process model until a final business process model is obtained to complete the optimization of the business process.

[0013] Further, the step of identifying abnormal activities in the set of active nodes to generate an abnormal activity set includes:

[0014] Based on attribute statistics, each category of abnormal activities in the activity node set is identified, and the abnormal activities of each category are integrated to generate an abnormal activity set.

[0015] Furthermore, after the step of identifying various categories of abnormal activities in the set of active nodes based on attribute statistics and integrating the various categories of abnormal activities to generate an abnormal activity set, the step further includes:

[0016] Calculate the filtering feature value of each abnormal activity in the abnormal activity set.

[0017] Furthermore, the attribute statistics include at least one of frequency statistics, time consumption statistics and sales statistics.

[0018] Furthermore, the step of calculating the effectiveness of abnormal activity identification according to the abnormal activity set includes:

[0019] Calculating the abnormal activity efficiency of each abnormal activity in the abnormal activity set based on data envelopment analysis;

[0020] The abnormal activity recognition effectiveness is calculated based on the abnormal activity efficiency.

[0021] Furthermore, the step of determining the set of activity nodes corresponding to the business process includes:

[0022] Analyze the event log data corresponding to the business process to extract all trajectory information;

[0023] An activity node set corresponding to the business process is generated based on all the trajectory information.

[0024] In addition, to achieve the above-mentioned purpose, the present invention further provides a business process optimization device, and the business process optimization device of the present invention includes:

[0025] A determination module is used to determine the set of activity nodes corresponding to the business process;

[0026] an identification module, configured to identify abnormal activities in the set of activity nodes to generate an abnormal activity set;

[0027] A calculation module, configured to calculate the effectiveness of abnormal activity identification according to the abnormal activity set;

[0028] The optimization module is used to construct a preliminary process model and evaluate the effectiveness of the abnormal activity identification based on the preliminary process model to optimize the business process.

[0029] Each functional module of the business process optimization device of the present invention implements the steps of the business process optimization method as described above during operation.

[0030] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal device, which includes: a memory, a processor, and a business process optimization program stored in the memory and executable on the processor, and the business process optimization program, when executed by the processor, implements the steps of the business process optimization method as described above.

[0031] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a business process optimization program is stored, and when the business process optimization program is executed by a processor, the steps of the business process optimization method as described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also provides a business process optimization program product, including a business process optimization program, which implements the steps of the above-mentioned business process optimization method when executed by a processor.

[0033] The present invention proposes a business process optimization method, apparatus, terminal device and computer-readable storage medium, the method comprising: determining a set of activity nodes corresponding to the business process; identifying abnormal activities in the activity node set to generate an abnormal activity set; calculating the effectiveness of abnormal activity identification based on the abnormal activity set; constructing a preliminary process model and evaluating the effectiveness of abnormal activity identification based on the preliminary process model to optimize the business process.

[0034] The present invention first determines the set of activity nodes corresponding to the business process, then identifies each abnormal activity in the set of activity nodes to generate an abnormal activity set corresponding to the entire business process, and then further calculates the effectiveness of abnormal activity identification for each abnormal activity based on the abnormal activity set based on data envelopment analysis. Finally, based on the construction of a preliminary process model, the preliminary process model is used to evaluate the effectiveness of abnormal activity identification, so as to iteratively update the process model to carry out an optimization process for the entire business process.

[0035] In this way, the present invention determines the effectiveness of abnormal activity filtering from an efficiency perspective based on data envelopment analysis, thereby being able to discover more accurate process models from event logs and provide decision support for process optimization, effectively improving the efficiency of optimizing business processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the structure of the hardware operation of the terminal device involved in the embodiment of the present invention;

[0037] Figure 2 It is a flow chart of an embodiment of a method for optimizing a business process of the present invention;

[0038] Figure 3 It is a structural diagram of an embodiment of a business process optimization device of the present invention.

[0039] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0041] like Figure 1 As shown, Figure 1 It is a structural diagram of the hardware operating environment of the terminal device involved in the embodiment of the present invention.

[0042] It should be noted that Figure 1 That is, it is a schematic diagram of the structure of the hardware operating environment of the terminal device. The terminal device in the embodiment of the present invention can be a PC, a portable computer and other terminal devices.

[0043] like Figure 1 As shown, the terminal device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Dis l ay), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0044] Those skilled in the art will understand that Figure 1The terminal device structure shown in the figure does not constitute a limitation on the terminal device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0045] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a distributed task processing program. The operating system is a program that manages and controls the hardware and software resources of the sample terminal device, and supports the operation of the distributed task processing program and other software or programs.

[0046] exist Figure 1 In the terminal device shown, the user interface 1003 is mainly used to communicate data with each terminal; the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; and the processor 1001 can be used to call the optimization program of the business process stored in the memory 1005 and perform the following operations:

[0047] Determine the set of activity nodes corresponding to the business process;

[0048] identifying abnormal activities in the set of activity nodes to generate an abnormal activity set;

[0049] Calculating the abnormal activity recognition effectiveness according to the abnormal activity set;

[0050] A preliminary process model is constructed and the effectiveness of the abnormal activity identification is evaluated based on the preliminary process model to optimize the business process.

[0051] Further, the processor 1001 may call the optimization program of the business process stored in the memory 1005, and further perform the following operations:

[0052] Calculating the abnormal activity efficiency of each abnormal activity in the abnormal activity set based on data envelopment analysis;

[0053] The abnormal activity recognition effectiveness is calculated based on the abnormal activity efficiency.

[0054] Further, the processor 1001 may call the optimization program of the business process stored in the memory 1005, and further perform the following operations:

[0055] constructing a preliminary process model based on trajectory information and activity nodes extracted from event log data corresponding to the business process;

[0056] The effectiveness of identifying abnormal activities is evaluated based on the preliminary process model to perform step-by-step filtering of abnormal activities, so as to iteratively update the preliminary process model until a final business process model is obtained to complete the optimization of the business process.

[0057] Further, the processor 1001 may call the optimization program of the business process stored in the memory 1005, and further perform the following operations:

[0058] Based on attribute statistics, each category of abnormal activities in the activity node set is identified, and the abnormal activities of each category are integrated to generate an abnormal activity set.

[0059] Further, the processor 1001 may call the optimization program of the business process stored in the memory 1005, and after executing the steps of identifying various categories of abnormal activities in the activity node set based on attribute statistics and integrating the various categories of abnormal activities to generate an abnormal activity set, further perform the following operations:

[0060] Calculate the filtering feature value of each abnormal activity in the abnormal activity set.

[0061] Furthermore, the attribute statistics include at least one of frequency statistics, time consumption statistics and sales statistics.

[0062] Further, the processor 1001 may call the optimization program of the business process stored in the memory 1005, and further perform the following operations:

[0063] Analyze the event log data corresponding to the business process to extract all trajectory information;

[0064] An activity node set corresponding to the business process is generated based on all the trajectory information.

[0065] Based on the above structure, various embodiments of the business process optimization method of the present invention are proposed.

[0066] Please refer to Figure 2 , Figure 2 It is a flowchart of the first embodiment of the business process optimization method of the present invention.

[0067] The embodiments of the present invention provide embodiments of a method for optimizing a business process. It should be noted that, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0068] The method for optimizing the business process in the embodiment of the present invention is applied to the above-mentioned terminal device. The terminal device in the embodiment of the present invention can be a PC, a portable computer or other terminal device, and no specific limitation is made here.

[0069] The business process optimization method of this embodiment includes:

[0070] Step S100, determining a set of activity nodes corresponding to a business process;

[0071] When the terminal device is identifying the evolution of a business process, it first determines the activity node set to which all the activity nodes in the event log corresponding to the business process belong.

[0072] It should be noted that, in this embodiment, the terminal device determines the set of active nodes by extracting track information from the event log corresponding to the business process.

[0073] Furthermore, in some feasible embodiments, the above step S100 may include:

[0074] Step S101, analyzing the event data in the event log corresponding to the business process to extract all the track information;

[0075] Step S102: Generate an activity node set corresponding to the business process based on all the track information.

[0076] It should be noted that, in this embodiment, the event data in the above-mentioned event log can usually be obtained at any time from information systems such as ERP (Enterprise Resource Planning), CRM (Customer Relationship Management) or BPM (BPM-Business Process Management), and the event data may specifically include: case name, case ID, timestamp, activity, resource, status and other attribute information.

[0077] In this embodiment, the terminal device analyzes the event data in the event log corresponding to the business process (i.e., the log data of the business process) to extract all trajectory information and count the number of cases for each trajectory. Then, for each case, the terminal device extracts a limited activity sequence A1=<a11,a12,...,a1n> Form a track information. In this way, all track information corresponding to the event log L can be expressed by the activity sequence A and the number of tracks in the following form:

[0078] <l> ＝[ <a1> n1 , <a2> n1 ，...， <am> nl ]

[0079] Thus, the terminal device can determine the active node set as: A <l>=A1∪A2∪…∪Am, that is: the non-repeating union of all activity nodes in all activity sequences.

[0080] Step S200, identifying abnormal activities in the set of active nodes to generate an abnormal activity set;

[0081] After determining the activity node set corresponding to the business process, the terminal device further identifies each activity node in the activity node set, thereby identifying all abnormal activities contained in the activity node set, and then integrating all abnormal activities to generate an abnormal activity set.

[0082] It should be noted that, in this embodiment, the terminal device identifies all abnormal activities in the active node set based on the attribute statistics method.

[0083] Further, in some feasible embodiments, the above step S200, identifying abnormal activities in the set of active nodes to generate an abnormal activity set, may specifically include:

[0084] Step S201 , identifying various categories of abnormal activities in the activity node set based on attribute statistics, and integrating the various categories of abnormal activities to generate an abnormal activity set.

[0085] In this embodiment, the terminal device identifies different categories of abnormal activities in the above-mentioned set of activity nodes based on attribute statistics, and then integrates the different categories of abnormal activities to generate a total set of abnormal activities.

[0086] Furthermore, in a feasible embodiment, the above-mentioned attribute statistics include at least one of frequency statistics, time consumption statistics and sales statistics.

[0087] That is, the attribute data statistics in the attribute statistics event log activity filtering technology of the terminal device include: frequency statistics.

[0088] For example, the terminal device can filter the event log activity based on frequency statistics by counting the case occurrence frequency of each activity in the trajectory information, sorting and statistically analyzing the event log activities according to the number of cases F, and automatically identifying the low-frequency activity set A. F .

[0089] Alternatively, the attribute data statistics in the attribute statistics event log activity filtering technology of the terminal device include: time consumption statistics.

[0090] It is feasible, for example, that the terminal device uses an event log activity filtering technology based on time-consuming statistics to automatically identify a time-consuming abnormal activity set A by counting the duration S of each activity in the trajectory information, and sorting and statistically analyzing the event log activities according to the duration (for example, filtering outliers based on percentiles or a certain standard deviation from the mean). S .

[0091] Alternatively, the attribute data statistics in the attribute statistics event log activity filtering technology of the terminal device include: sales statistics.

[0092] For example, the terminal device may filter the event log activity based on the amount statistics by counting the amount M involved in the case corresponding to each activity in the trajectory information = (M 1 , M 2 , M 3 , ..., M m ) and the sum of the amount For example, the sales amount of sales orders, the purchase amount in purchase orders, etc., and sort and statistically analyze the event log activities according to the total amount involved in the case, and automatically identify the activity set A with abnormally low amount M .

[0093] In addition, in some other feasible embodiments, the terminal device also uses event log activity filtering technology based on resource statistics to count the resources involved in the case corresponding to each activity in the trajectory information R=(R 1 , R 2 , ..., R r ) and the total amount of resources R′, such as the number of people involved, the number of approvals, or the cost, and sort and statistically analyze the event log activities according to the amount of resources R′ involved in the case to automatically identify the resource-intensive activity set A R .

[0094] The abnormal activity set is AA=A F ∪A S ∪A M ∪A R , and obtain all abnormal activities aa and their filtered feature value sets Faa, Saa, Maa, Raa.

[0095] Further, in some feasible embodiments, in the above step S201, after identifying various categories of abnormal activities in the set of activity nodes based on attribute statistics and integrating various categories of abnormal activities to generate an abnormal activity set, the business process optimization method of the present invention may further include:

[0096] Step S202: Calculate the filtering feature value of each abnormal activity in the abnormal activity set.

[0097] In this embodiment, after the terminal device identifies different categories of abnormal activities in the above-mentioned activity node set based on attribute statistics and then integrates the different categories of abnormal activities to generate a total set of abnormal activities, it immediately calculates the filtering feature value of each abnormal activity in the total set of abnormal activities.

[0098] Alternatively, in other feasible embodiments, the terminal device may also identify different categories of abnormal activities in the above-mentioned set of activity nodes based on attribute statistics, and then integrate the different categories of abnormal activities to generate a total set of abnormal activities. At the same time, each time an abnormal activity is identified, a filtering feature value of the abnormal activity is simultaneously calculated for the identified abnormal activity.

[0099] It should be noted that, in this embodiment, the terminal device can use any existing mature feature calculation method to calculate the filtering feature value of abnormal activities. It should be understood that based on different design requirements of actual applications, in different feasible implementations, the terminal device can use different methods to calculate the filtering feature value of abnormal activities, and the optimization method of the business process of the present invention does not limit the type and specific process of the method used by the terminal device to calculate the filtering feature value of abnormal activities.

[0100] Step S300, calculating the abnormal activity recognition effectiveness according to the abnormal activity set;

[0101] After identifying the abnormal activity and thus generating an abnormal activity set, the terminal device further calculates the abnormal activity identification effectiveness for identifying each abnormal activity based on the abnormal activity set based on data envelopment analysis.

[0102] Further, in some feasible embodiments, step S300 may include:

[0103] Step S301, calculating the abnormal activity efficiency of each abnormal activity in the abnormal activity set based on data envelopment analysis;

[0104] Step S302, calculating the abnormal activity recognition effectiveness according to the abnormal activity efficiency.

[0105] In this embodiment, in the abnormal activity identification effectiveness calculation phase, the terminal device further calculates the identification effectiveness function by determining the cost characteristics and the benefit characteristics and calculating the abnormal activity efficiency based on data envelopment analysis.

[0106] It should be noted that, in this embodiment, data envelopment analysis (DEA) is a quantitative analysis method that uses linear programming to evaluate the relative effectiveness of comparable units of the same type based on multiple input indicators and multiple output indicators. It is suitable for comprehensive effectiveness evaluation problems of multiple outputs and multiple inputs, and has an absolute advantage in processing effectiveness evaluation of multiple outputs and multiple inputs. There is no need to dimensionalize the data before applying the DEA method to establish a model.

[0107] Based on this, the terminal device sets the n abnormal activities in the abnormal activity set as n decision-making units (DMUs), and each DMU has m inputs and s outputs. Here, the number of cases Faa and the amount Maa to which the abnormal activity belongs are set as output items (income items); while the time consumption Saa and resource cost Raa are set as input items (cost items). 2 R data envelopment analysis model. That is:

[0108] Let x ij =(i=1, ...m, j=1, ..., n) represents the i-th input amount of the j-th DMU, ​​y rj =(r=1, ...s, j=1, ..., n) represents the rth output of the jth DMU, ​​v i =(i=1, ..., m) represents the weight of the i-th input, u r =(r=1, ..., s) represents the weight of the r-th output. The vector X j , Y j (j=1, ..., n) represent the input and output vectors of decision unit j, v and u represent the input and output weight vectors, respectively. Then:

[0109] X j =(x 1j , x 2j , ..., x mj );

[0110] Y j =(y 1j ,y 2j , ..., y sj );

[0111] u=(u 1 ,u 2 ,...,u m );

[0112] v=(v 1 ,v 2 ,...,v s ).

[0113] Define the efficiency evaluation index h of decision-making unit j j for:

[0114]

[0115] Evaluation and decision making unit 0 The mathematical model of efficiency is:

[0116]

[0117]

[0118] Among them, the above evaluation decision unit j 0 Mathematical models of efficiency are generally used to measure the production efficiency of some decision-making departments. Among the results, some activities that achieve 100% efficiency are called relatively efficient activities, while other units with efficiency scores below 100% are called inefficient activities.

[0119] Further, calculate the abnormal activity filtering effectiveness function F e (aa), the calculation formula is:

[0120] F e (aa) = 1-E f (aa)

[0121] Among them, F f (aa) is the final efficiency value of each abnormal activity in the data envelopment analysis model results.

[0122] Step S400, constructing a preliminary process model and evaluating the effectiveness of identifying abnormal activities based on the preliminary process model to optimize the business process.

[0123] After calculating the effectiveness of identifying abnormal activities for each abnormal activity, the terminal device further constructs a preliminary process model and evaluates the effectiveness of identifying the abnormal activity based on the preliminary process model to optimize the business process.

[0124] Further, in some feasible embodiments, step S400 may include:

[0125] Step S401, constructing a preliminary process model according to the trajectory information and activity nodes extracted from the event log data corresponding to the business process;

[0126] Step S402: evaluating the effectiveness of identifying abnormal activities based on the preliminary process model to perform step-by-step filtering of abnormal activities, so as to iteratively update the preliminary process model until a final business process model is obtained to complete the optimization of the business process.

[0127] In this embodiment, the terminal device evaluates the effectiveness of abnormal activities by establishing a preliminary process model, thereby gradually filtering abnormal activities to optimize a more accurate business process.

[0128] Optionally, for example, the terminal device uses the trajectory information and active nodes extracted from the event log data to establish a preliminary process model based on the Petri net (a mathematical representation of discrete parallel systems. The Petri net was invented by Carl A. Petri in the 1960s and is suitable for describing asynchronous and concurrent computer system models. The Petri net has both rigorous mathematical expressions and intuitive graphical expressions, rich system description methods and system behavior analysis techniques, and provides a solid conceptual foundation for computer science).

[0129] Afterwards, the terminal device sorts the abnormal activity filtering effectiveness in the abnormal activity set from high to low, sets a threshold, and determines the abnormal activities that exceed the threshold as the final abnormal activity set. By gradually filtering the activities with the highest abnormal activity filtering effectiveness, iteratively updating the process model, and finally obtaining an optimized business process model.

[0130] In this embodiment, during the process of identifying the evolution of a business process, the terminal device first determines the activity node set to which all activity nodes in the event log corresponding to the business process belong; after determining the activity node set corresponding to the business process, the terminal device further identifies each activity node in the activity node set, thereby identifying all abnormal activities contained in the activity node set, and then integrating all abnormal activities to generate an abnormal activity set; after identifying the abnormal activities and generating the abnormal activity set, the terminal device further calculates the effectiveness of abnormal activity identification for each abnormal activity based on the abnormal activity set based on data envelopment analysis; after calculating the effectiveness of abnormal activity identification for each abnormal activity, the terminal device further constructs a preliminary process model and evaluates the effectiveness of abnormal activity identification based on the preliminary process model to optimize the business process.

[0131] In this way, the present invention determines the effectiveness of abnormal activity filtering from an efficiency perspective based on data envelopment analysis, thereby being able to discover more accurate process models from event logs and provide decision support for process optimization, effectively improving the efficiency of optimizing business processes.

[0132] In addition, an embodiment of the present invention further provides a business process optimization device, and the business process optimization device of the present invention includes:

[0133] A determination module is used to determine the set of activity nodes corresponding to the business process;

[0134] an identification module, configured to identify abnormal activities in the set of activity nodes to generate an abnormal activity set;

[0135] A calculation module, configured to calculate the effectiveness of abnormal activity identification according to the abnormal activity set;

[0136] The optimization module is used to construct a preliminary process model and evaluate the effectiveness of the abnormal activity identification based on the preliminary process model to optimize the business process.

[0137] Optionally, the computing module comprises:

[0138] A first calculation unit, configured to calculate the abnormal activity efficiency of each abnormal activity in the abnormal activity set based on data envelopment analysis;

[0139] The second calculation unit is used to calculate the abnormal activity recognition effectiveness according to the abnormal activity efficiency.

[0140] Optionally, the optimization module includes:

[0141] A model building unit, used to build a preliminary process model based on the trajectory information and activity nodes extracted from the event log data corresponding to the business process;

[0142] A process optimization unit is used to evaluate the effectiveness of identifying abnormal activities based on the preliminary process model to perform step-by-step filtering of abnormal activities, so as to iteratively update the preliminary process model until a final business process model is obtained to complete the optimization of the business process.

[0143] Optionally, the identification module is further configured to:

[0144] Based on attribute statistics, each category of abnormal activities in the activity node set is identified, and the abnormal activities of each category are integrated to generate an abnormal activity set.

[0145] Optionally, the computing module of the business process optimization device of the present invention is further used for:

[0146] Calculate the filtering feature value of each abnormal activity in the abnormal activity set.

[0147] Optionally, the attribute statistics include at least one of frequency statistics, time consumption statistics and sales statistics.

[0148] Optionally, a module is determined, including:

[0149] An analysis unit, used for analyzing the event log data corresponding to the business process to extract all the track information;

[0150] A set generating unit is used to generate an activity node set corresponding to the business process based on all the track information.

[0151] The steps implemented by each functional module of the business process optimization device of the present invention during operation can refer to the various embodiments of the business process optimization method of the present invention, and will not be repeated here.

[0152] In addition, an embodiment of the present invention also proposes a terminal device, which includes: a memory, a processor, and a business process optimization program stored in the memory and executable on the processor, and the business process optimization program, when executed by the processor, implements the steps of the business process optimization method as described above.

[0153] Among them, the steps implemented when the business process optimization program running on the processor is executed can refer to the various embodiments of the business process optimization method of the present invention, and will not be repeated here.

[0154] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, which is applied to a computer. The computer-readable storage medium may be a non-volatile computer-readable storage medium. A business process optimization program is stored on the computer-readable storage medium. When the business process optimization program is executed by a processor, the steps of the business process optimization method as described above are implemented.

[0155] In addition, an embodiment of the present invention also proposes a business process optimization program product, which includes an architecture program for store visitor information. When the architecture program for store visitor information is executed by a processor, the steps of the architecture method for store visitor information as described above are implemented.

[0156] Among them, the steps implemented when the business process optimization program running on the processor is executed can refer to the various embodiments of the business process optimization method of the present invention, and will not be repeated here.

[0157] It should be noted that, in this article, the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0158] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0159] 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 invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0160] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.< / l> < / am> < / a2> < / a1> < / l>

Claims

1. A method for optimizing a business process, characterized in that: The business process optimization method includes: Determine the set of activity nodes corresponding to the business process; identifying abnormal activities in the set of activity nodes to generate an abnormal activity set; Calculating the abnormal activity recognition effectiveness according to the abnormal activity set; Constructing a preliminary process model and evaluating the effectiveness of identifying abnormal activities based on the preliminary process model to optimize business processes; The step of calculating the abnormal activity identification effectiveness according to the abnormal activity set comprises: Calculating the abnormal activity efficiency of each abnormal activity in the abnormal activity set based on data envelopment analysis; The effectiveness of abnormal activity recognition is calculated based on the abnormal activity efficiency; The step of constructing a preliminary process model and evaluating the effectiveness of identifying abnormal activities based on the preliminary process model to optimize the business process includes: constructing a preliminary process model based on trajectory information and activity nodes extracted from event log data corresponding to the business process; The effectiveness of identifying abnormal activities is evaluated based on the preliminary process model to perform step-by-step filtering of abnormal activities, so as to iteratively update the preliminary process model until a final business process model is obtained to complete the optimization of the business process.

2. The business process optimization method according to claim 1, characterized in that: The step of identifying abnormal activities in the set of active nodes to generate an abnormal activity set comprises: Based on attribute statistics, each category of abnormal activities in the activity node set is identified, and the abnormal activities of each category are integrated to generate an abnormal activity set.

3. The business process optimization method according to claim 2, characterized in that: After the step of identifying various categories of abnormal activities in the set of active nodes based on attribute statistics and integrating the various categories of abnormal activities to generate an abnormal activity set, the method further includes: Calculate the filtering feature value of each abnormal activity in the abnormal activity set.

4. The method for optimizing a business process according to claim 2 or 3, characterized in that: The attribute statistics include at least one of frequency statistics, time consumption statistics and sales statistics.

5. The business process optimization method according to claim 1, characterized in that: The step of determining the set of activity nodes corresponding to the business process includes: Analyze the event log data corresponding to the business process to extract all trajectory information; An activity node set corresponding to the business process is generated based on all the trajectory information.

6. A device for optimizing a business process, characterized in that: The business process optimization device includes: A determination module is used to determine the set of activity nodes corresponding to the business process; an identification module, configured to identify abnormal activities in the set of activity nodes to generate an abnormal activity set; A calculation module, configured to calculate the effectiveness of abnormal activity identification according to the abnormal activity set; An optimization module, used for constructing a preliminary process model and evaluating the effectiveness of identifying abnormal activities based on the preliminary process model to optimize the business process; The computing module comprises: A first calculation unit, configured to calculate the abnormal activity efficiency of each abnormal activity in the abnormal activity set based on data envelopment analysis; A second calculation unit, configured to calculate abnormal activity recognition effectiveness according to the abnormal activity efficiency; The optimization module comprises: A model building unit, used to build a preliminary process model based on the trajectory information and activity nodes extracted from the event log data corresponding to the business process; A process optimization unit is used to evaluate the effectiveness of identifying abnormal activities based on the preliminary process model to perform step-by-step filtering of abnormal activities, so as to iteratively update the preliminary process model until a final business process model is obtained to complete the optimization of the business process.

7. A terminal device, characterized in that: The terminal device includes: a memory, a processor, and a business process optimization program stored in the memory and executable on the processor. When the business process optimization program is executed by the processor, the steps of the business process optimization method as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a business process optimization program, and when the business process optimization program is executed by a processor, the steps of the business process optimization method according to any one of claims 1 to 5 are implemented.

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