Object-centered business process violation inspection method and system based on Tokine replay

Abstract

The invention discloses an object-centered business process violation inspection method and system based on Tokine replay, and belongs to the field of process mining, and the method comprises the following steps: constructing an object graph from an object-centered business process event log, and extracting process execution; deleting cross-layer redundant dependencies in process execution, dividing the process execution according to echeles, and obtaining a binding sequence; performing Tokine replay in the multi-object Petri net model according to the binding sequence to obtain a replay result and a deviation result; and quantifying the compliance according to the fitting degree, and displaying a part with serious deviation in the process by using a local diagnosis method to obtain an object as a center business process violation inspection result. The system comprises an object graph and process execution acquisition module, a binding sequence acquisition module, a replay result and deviation result acquisition module and an object center business process violation check result acquisition module. For the object-centered business process, the method provided by the invention improves the accuracy of the compliance check of the object-centered business process.

Description

Technical Field

[0001] The present invention belongs to the technical field of process mining, and in particular relates to an object-centric business process violation checking method and system based on token replay. Background Art

[0002] Traditional process mining is based on the concept of a single case and can only construct a process model from a single object perspective. As business processes become more and more complex, they usually involve multiple process objects, which is called object-centric business processes. This means that complex business processes are divided into multiple objects with clear business attributes, and business processes are built and managed around the interactive dependencies between these objects. Existing technologies cannot effectively handle situations where logs contain multiple process objects.

[0003] Therefore, object-centric process mining, which deals with multi-object interaction behaviors, came into being. In order to ensure that this technology operates in compliance in complex processes, the compliance issues of object-centric process mining have received more and more attention. However, there are many disadvantages in the existing technologies when dealing with multi-object compliance issues. For example, the existing technologies can only use the method of flattening multi-object complex processes to perform compliance checks, resulting in the loss of process interaction information that exists on certain objects, resulting in the inability to accurately identify deviations and the difficulty in confirming the source of deviations. Therefore, there is an urgent need for a new compliance check method that can be applied to object-centric business processes, thereby improving the accuracy of compliance checks on object-centric business processes. Summary of the invention

[0004] In order to solve the problem that traditional methods cannot accurately identify deviations and are difficult to confirm the source of deviations, the present invention proposes an object-centric business process violation inspection method and system based on token replay, which can accurately and effectively identify object-centric business process violations, display the severity of deviations, quickly locate problem links, and provide support for multi-object complex process analysis, which is practical.

[0005] The technical solution of the present invention is as follows: A token replay-based object-centric business process violation checking method comprises the following steps: Step 1: Obtain object-centric business process event logs, analyze dependencies between objects to construct object graphs, and then use connected component extraction methods to obtain process execution; Step 2: Use the transitive reduction method to remove cross-layer redundant dependencies, and then use the echelon partitioning mechanism to obtain the binding sequence; Step 3: Use the binding execution rules to perform token replay on the multi-object Petri net to obtain the replay result and deviation result; Step 4: Calculate the fitting result and the local diagnosis result to obtain the violation inspection result of the object-centered business process.

[0006] Furthermore, the specific process of step 1 is as follows: Step 1.1: The object obtained is the central business process event log It consists of a set of events representing operations, which include activities, occurrence time, end time and basic attributes of associated objects; Step 1.2: Construct an object-centric business process event log by analyzing the dependencies between objects The object graph is: ; in, It is an undirected graph node set consisting of all objects in the object-centric business process event log; It is an undirected graph edge set consisting of several pairs of objects with dependency relationships; The calculation formula is: ; in, , For events Two different objects are associated; The object represents all events in the central business process event log; Represents an object-centric business process event log Events All associated objects are calculated using the formula: ; in, Representation and Objects A linked sequence of events; Step 1.3: Use the connected component extraction method to obtain process execution; the specific process is as follows: First, the formal connected component extraction method is: ; in, is the connected component extraction method; represents the process execution obtained from the maximum connected subgraph; is the set of objects in the maximum connected subgraph; Then, according to The directed graph of the process execution is obtained. The definition is as follows: ; in, It is a directed graph node collection, connected by object-centric business process event log All events of any object in; It is a set of directed graph edges, consisting of pairs of events with direct following relationships; , The calculation formulas are: ; ; in, is an empty set; Represents an object-centric business process event log The direct follow-up relationship between events is calculated as follows: ; in, Different from the event events; For the events; For the events; For the an event.

[0007] Furthermore, the specific process of step 2 is as follows: Step 2.1, using the transitive reduction method to delete the cross-layer redundant dependencies in the object graph, and obtain a minimum subgraph that maintains the transitive closure characteristics of the original graph; the cross-layer redundant dependencies refer to the dependencies between nodes in the object graph realized by indirect paths, and maintaining the transitive closure characteristics of the original graph means that the reachability relationship between nodes in the graph remains unchanged after deleting the redundant dependencies; Step 2.2: Use the echelon division mechanism to divide the process execution; the specific process is: The no-predecessor node identification algorithm is iteratively implemented, and each time all the current no-predecessor nodes are obtained as an echelon, and the process execution is divided into several logically independent event echelons, which are disordered within the echelon and follow the established sequential logic between echelons; the no-predecessor node identification algorithm is to identify the nodes that currently have no predecessor nodes in the context of the graph and store them in a list; Step 2.3 Map each logically independent event echelon to its own binding to obtain the corresponding binding sequence; define the binding sequence as follows: ; in, For the A change, For the The specific objects of each object type consumed when a transition is executed; For the The binding corresponding to each transition.

[0008] Furthermore, the specific process of step 3 is as follows: Step 3.1: Define a multi-object Petri net as follows: ; in, It is a Petri net; It is the mapping function from library to object type; is a set of variable arcs, is the set of all arcs in a multi-object Petri net; is the cardinality labeling function of the arc; and Represent the start mark and end mark respectively; The specific mapping relationship is: ; in, For the library collection, is a collection of object types; Step 3.2: Based on the binding sequence, the process execution is performed on the multi-object Petri net according to the binding execution rules to perform token replay, and the replay result and deviation result of each process execution are obtained; The replay result is the number of tokens generated, consumed, missing, and remaining after token replay for each process execution; Number of tokens generated Defined as: ; in, For Change The back set of the library is collected; The specific objects of each object type consumed when the transition is executed; It is a multi-object Petri net Token set of ; Token, indicating the library Has an object ; Number of tokens consumed Defined as: ; in, For Change The front set of libraries is collected; Number of missing tokens Defined as: ; The remaining tokens Defined as: ; Deviation results include transitions that are not enabled normally during process execution and missing token information; The judgment rules for transitions that are not enabled normally are: when When binding In the logo Enable, change In the logo The following is the new identifier obtained by executing the enabled binding: , the calculation formula is: ; when When binding Unable to identify Enable, change For changes that are not normally enabled; Missing token information Defined as: ; Missing token information is a transition In the library The object required for binding to work properly is missing .

[0009] Furthermore, the specific process of step 4 is as follows: Step 4.1, calculate the degree of fit, the formula is as follows: ; in, is the degree of fit; The object represents the number of process executions in the central business process event log; Indicates The number of tokens missing from a process execution in the replay; Indicates The number of tokens consumed by a process execution in replay; Indicates The number of tokens remaining in the replay for a process execution; Indicates The number of tokens generated by a process execution in a replay; Compliance is quantified based on the fit measure, and the closer the fit is to 1, the better the compliance and the less the deviation; Step 4.2: Use the local diagnosis method to calculate the local diagnosis result. The formula is: ; ; in, Execute for the process The local diagnosis results of the abnormally enabled transition consistency; Event log for object-centric business processes The local diagnosis results of the abnormally enabled transition consistency; yes Changes that are not enabled normally; It is a set of transitions that are not enabled normally; yes In process execution The number of tokens missing in the replay; yes In process execution The number of tokens consumed in the replay; Represents an object-centric business process event log Included The number of process executions corresponding to the log activity is calculated as follows: ; in, for Corresponding log activities; The replay results, deviation results, fit results and local diagnosis results are the required object-centric business process violation inspection results.

[0010] A token replay-based object-centric business process violation checking system adopts the token replay-based object-centric business process violation checking method as described above. The input of the system is an object-centric business process event log and a multi-object Petri net, and the output is an object-centric business process violation checking result. The system includes the following modules: The object graph acquisition and process execution module is used to acquire the object graph according to the dependencies between objects in the object-centric business process event log, and then obtain the process execution from the object graph using the connected component extraction method; The binding sequence acquisition module is used to delete cross-layer redundant dependencies in the process execution according to the transitive reduction method to obtain a simplified process execution, and then use the echelon partitioning mechanism to divide the simplified process execution to obtain a binding sequence; A module for obtaining replay results and deviation results is used to perform token replay on a multi-object Petri net model according to binding execution rules, and obtain replay results and deviation results in the violation inspection results of the object-centric business process; The object to be acquired is the central business process violation inspection result module, which is used to substitute the obtained replay results and deviation results into the defined fit formula to quantify compliance, and then use the local diagnosis method to display the part of the process with serious deviations. The object to be acquired is the fit results and local diagnosis results in the central business process violation inspection results.

[0011] The beneficial technical effects brought by the present invention are as follows: for object-centric business processes, the present invention provides a solution, which is successfully applied to the problem of compliance inspection of object-centric business processes and is feasible; for the problem that existing compliance inspection technologies need to flatten object-centric business processes and object interaction information is lost, the method of the present invention can fully extract the interaction relationship between objects to discover the actual process execution and is comprehensive; for the problem that existing compliance inspection technologies cannot accurately identify deviations and are difficult to confirm the source of deviations, the method of the present invention can accurately identify non-compliant behaviors and reduce the possibility of misjudgment or missed judgment and is accurate; for the problem that existing compliance inspection technologies cannot accurately identify deviations and are difficult to confirm the source of deviations To identify the source of deviation, the method of the present invention can quickly locate the problem link and promptly correct the problem in process execution through quantifying deviation and local diagnosis, and is highly efficient; in view of the problem that existing compliance inspection technologies are difficult to quantify deviations in object-centric business processes, the method of the present invention uses fitting metrics to quantify deviations and uses local diagnosis methods to display the severity of deviations in misfit process execution, and flexibly responds to compliance inspection requirements of different object-centric business processes, and is adaptable and scalable; the present invention can be applied to object-centric business scenarios or fields, such as machine equipment manufacturing processes, fan maintenance processes, medical business processes, service processes, etc., and has high application value and broad prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The flowchart of the object-centric business process violation checking method based on token replay of the present invention.

[0013] Figure 2 This is an object graph obtained using the method of the present invention in an embodiment of the present invention.

[0014] Figure 3 This is a flow chart obtained by using the method of the present invention in an embodiment of the present invention.

[0015] Figure 4 This is a process execution diagram after deleting cross-layer redundant dependencies obtained by using the method of the present invention in an embodiment of the present invention.

[0016] Figure 5 This is a standard process model diagram for a procurement and sales scenario in an embodiment of the present invention.

[0017] Figure 6 It is a multi-object Petri net diagram of a standard process model of a procurement and sales scenario using the method of the present invention in an embodiment of the present invention.

[0018] Figure 7 It is an architecture diagram of the object-centric business process violation checking system based on token replay of the present invention.

[0019] Figure 8It is a multi-object Petri net diagram of the invoice object type model in the comparative experiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments: The flow chart of the present invention is as follows Figure 1 As shown, starting from the object-centric business process event log, first obtain the object graph and process execution, then obtain the binding sequence, then obtain the token replay result and deviation result, then quantify the compliance and display the serious deviation part, and finally output the object-centric business process violation inspection result. The present invention also designs an object-centric business process violation inspection system based on token replay and applied to the object-centric business process according to the above process. The main functional modules of the system include: obtaining object graph and process execution module, obtaining binding sequence module, obtaining replay result and deviation result module, and obtaining object-centric business process violation inspection result module.

[0021] A token replay-based object-centric business process violation checking method comprises the following steps: Step 1: Get the object-centric business process event log, analyze the dependencies between objects to construct an object graph, and then use the connected component extraction method to obtain the process execution. The specific process is: Step 1.1: The object obtained is the central business process event log It consists of a set of events representing operations, which include activities, occurrence time, end time and basic attributes of associated objects; Step 1.2: The object graph is an undirected graph that describes the dependencies between objects in the object-centric business process event log. The object-centric business process event log is constructed by analyzing the dependencies between objects. The object graph is: ; in, It is an undirected graph node set consisting of all objects in the object-centric business process event log; It is an undirected graph edge set consisting of several pairs of objects with dependency relationships; The calculation formula is: ; in, , For events Two different objects are associated; The object represents all events in the central business process event log; Represents an object-centric business process event log Events All associated objects are calculated using the formula: ; in, Representation and Objects The associated event sequence, the specific mapping relationship is: ; Step 1.3, the connected component extraction method is a method for obtaining process execution from all the largest connected subgraphs of the object graph. The process execution is a directed graph based on the dependency relationship between objects in the largest connected subgraph describing the dependency and interaction information between process events associated with it. The process of obtaining process execution using the connected component extraction method in the present invention is as follows: First, the formal connected component extraction method is: ; in, is the connected component extraction method; is an object graph The maximum connected subgraph of ; represents the process execution obtained from the maximum connected subgraph; is the set of objects in the maximum connected subgraph; Then, according to The directed graph of obtains the process execution and defines for: ; in, It is a directed graph node collection, connected by object-centric business process event log All events of any object in; It is a set of directed graph edges, consisting of pairs of events with direct following relationships; , The calculation formulas are: ; ; in, Represents an object-centric business process event log The direct follow-up relationship between events is calculated as follows: ; in, Different from the event events; For the events; For the events; For the an event.

[0022] The method of the present invention is applicable to object-centric business processes, that is, any given business process, as long as the business process belongs to an object-centric business process, then the business process can be checked for business process violations using the method of the present invention. For example, the standard process of purchase and sales is an object-centric business process. In the standard process of purchase and sales, as shown in Table 1, the object-centric business process event log L1 contains 9 activities (i.e. corresponding to to ), 5 object types and 7 objects. The activities are as follows: create customer order, create supplier order, receive supplier package, open package and put into storage, create delivery note, pack for shipment, deliver, receive payment, clear delivery note. The object types are as follows: customer order (Order), supplier order (Supplier Order), delivery note (Invoice), product details (Item), payment (Payment). The objects are as follows: customer order object O1, supplier order object A, delivery note object I1, product details object X1, product details object X2, product details object X3, payment object P1.

[0023] Table 1 Object-centric business process event log L1 .

[0024] The object graph obtained by the method of the present invention like Figure 2 As shown, its undirected graph node set is ={A, X1,X2, X3, O1, I1, P1}, the edge set of the undirected graph is = {{A, X1, X2, X3}, {O1, X1, X2, X3},{O1, I1}, {I1, P1}}; due to the object graph It is a maximum connected subgraph itself, so the method of the present invention can only obtain one process execution ,like Figure 3 As shown, its directed graph node set is , the directed graph edge set is .

[0025] Step 2: Execute the process obtained in step 1, use the transfer-reduce method to remove cross-layer redundant dependencies, and then use the echelon partitioning mechanism to obtain the binding sequence. The specific process is: Step 2.1, using the transitive reduction method to delete cross-layer redundant dependencies; the specific process is: deleting the cross-layer redundant dependencies in the object graph to obtain a minimum subgraph that maintains the transitive closure characteristics of the original graph, wherein the cross-layer redundant dependencies refer to the dependencies between nodes in the object graph realized by indirect paths, and maintaining the transitive closure characteristics of the original graph means that the reachability relationship between nodes in the graph remains unchanged after deleting the redundant dependencies; Step 2.2: Use the echelon division mechanism to divide the process execution; the specific process is: The no-predecessor node identification algorithm is iteratively implemented, and each time all the current no-predecessor nodes are obtained as an echelon, and the process execution is divided into several logically independent event echelons, which are disordered within the echelon and follow the established sequential logic between echelons; the no-predecessor node identification algorithm is to identify the nodes that currently have no predecessor nodes in the context of the graph and store them in a list; Step 2.3 Map each logically independent event echelon to its own binding to obtain the corresponding binding sequence; define the binding sequence for: ; in, For the A change, For the The specific objects of each object type (consistent with the object type of the set library before this transition) consumed when a transition is executed, the number of transitions is the same as the number of events, that is, ; For the The binding corresponding to the transition, that is, if the preceding set of this transition contains If the objects of each object type are bound, the transition is in a pending state; enabling means that when the conditions for the transition to be executed are met, the transition is in a state where it can occur.

[0026] The process after deleting cross-layer redundant dependencies obtained by the method of the present invention is executed as follows Figure 4 As shown, due to the event and events Events available So we use the transitive reduction method to remove the directed graph edges that cause redundant dependencies across layers. ; The echelon division mechanism is used to handle the process execution after deleting cross-layer redundant dependencies. The event echelons after division are: , the corresponding binding sequence echelons obtained are: [[('CO', {'Order': ['O1']}), ('SO', {'Supplier Order': ['A']}), ('RT', {'Payment': ['P1']})], [('RS', {'Item': ['X1', 'X2', 'X3'], 'Supplier Order': ['A']}), ('CI', {'Invoice': ['I1'], 'Order': ['O1']})], [('UP', {'Item': ['X1'], 'Supplier Order': ['A']}), ('RI', {'Payment': ['P1'], 'Invoice': ['I1']})],[('PS', {'Item': ['X1', 'X2', 'X3'], 'Order': ['O1']})], [('SP', {'Order': ['O1']})]].

[0027] Step 3: Based on the binding sequence obtained in step 2, use the binding execution rules to perform token replay on the multi-object Petri net to obtain the replay result and deviation result. The specific process is: Step 3.1: Define a multi-object Petri net It is a six-tuple, specifically: ; in, It is a Petri net; It is the mapping function from library to object type; is a set of variable arcs, is the set of all arcs in a multi-object Petri net; is the cardinality labeling function of the arc; and Represent the start mark and end mark respectively. The mark is a multi-set of tokens and is defined as ; For identification; for multiple episodes of Token; The specific mapping relationship is: ; in, For the library collection, is a collection of object types; The specific mapping relationship is: ; Among them, the symbol " " is a variable arc label, indicating that this arc can transport one or more tokens each time. The definition of token is , indicating the library Has an object ; It is a multi-object Petri net The token set is calculated as follows: ; in, is a collection of objects; is the object type; Step 3.2: Based on the binding sequence, the process execution is performed on the multi-object Petri net according to the binding execution rules to perform token replay, and the replay result and deviation result of each process execution are obtained; The replay result is the number of tokens generated, consumed, missing, and remaining after token replay for each process execution; Number of tokens generated for: ; in, For Change The back set of the library is collected; The specific objects of each object type consumed when the transition is executed; For Change Execute the bindings that need to be satisfied; Number of tokens consumed for: ; in, For Change The front set of libraries is collected; Number of missing tokens for: ; The remaining tokens for: ; Deviation results are transitions that are not enabled properly during process execution and missing token information added to ensure its smooth execution; The judgment rules for transitions that are not enabled normally are: when When binding In the logo Enable, change In the logo The following is the new identifier obtained by executing the enabled binding: ,Right now , the specific calculation formula is: ; when When binding Unable to identify Enable, change For changes that are not normally enabled; Missing token information Defined as: ; Missing token information is a transition In the library The object required for binding to work properly is missing .

[0028] The standard process model for purchasing and sales scenarios is as follows: Figure 5 As shown in the figure, there are five object types in this process, namely, product details (i.e., Item), supplier (Supplier Order), customer order (Order), invoice (Invoice) and payment (Payment). The place and transition of each object type business process are marked with a unique background color. The label of the place indicates its object type, and the label of the transition indicates its corresponding log activity. The activities shared by two object types use two background colors. Figure 5 In the figure, Item1, Item2, Item3, and Item4 are four product details; Supplier Orde1, SupplierOrde2, Supplier Orde3, and Supplier Orde4 are four suppliers; Orde1, Orde2, Orde3, and Orde4 are four customer orders; Invoice1, Invoice2, and Invoice3 are three invoices; Payment1, Payment2, and Payment3 are three payments; SO is to create a supplier order; CO is to create a customer order; RT is to receive payment; RS is to receive the supplier package; CI is to create a shipping order; UP is to open the package; RI is to clear the shipping order; PS is to pack and ship; SP is to deliver.

[0029] Using the method of the present invention Figure 5 Token replay is performed on the model to obtain the multi-object Petri net of the standard process model of the purchasing and sales scenario. Figure 6 As shown, Figure 6 The binding sequence of the standard process model for the purchase and sales scenario is as follows in Table 2: Table 2 Binding sequence table of the standard process model for purchasing and sales scenarios .

[0030] According to Table 2, the binding sequence Figure 6The multi-object Petri net is used for token replay, and the number of generated tokens is 25, the number of consumed tokens is 25, the number of missing tokens is 2, and the number of remaining tokens is 2 (corresponding to the remaining two objects X2 and X3 in the Item2 library).

[0031] Due to the existence of missing tokens and remaining tokens, this process execution cannot be fully fitted when performing token replay on the standard process model of the purchase and sales scenario. The deviation results are shown in Table 3. The deviation results shown in Table 3 include three parts: process execution sequence number, abnormally enabled transition, and added missing token information. It shows that there is an abnormally enabled transition PS (i.e., packaging and shipping) in process execution 1. The reason for the abnormal enablement is that two X products (i.e., X3 and X2) that should be packaged and shipped are missing in the PS link. The specific content of Table 3 is as follows: Table 3 Deviation results table .

[0032] Step 4: Based on the replay results and deviation results obtained in step 3, the fit formula is used to quantify compliance, and then the local diagnosis method is used to display the part of the process with serious deviations, and the violation inspection results of the object-centered business process are obtained. In addition to the replay results and deviation results, the violation inspection results of the object-centered business process also include the fit results and local diagnosis results; the specific calculation process of the fit results and local diagnosis results is as follows: Step 4.1, define the fit formula as follows: ; in, is the degree of fit; The object represents the number of process executions in the central business process event log; Indicates The number of tokens missing from a process execution in the replay; Indicates The number of tokens consumed by a process execution in replay; Indicates The number of tokens remaining in the replay for a process execution; Indicates The number of tokens generated by a process execution in a replay; Compliance is quantified based on the goodness of fit value. The closer the goodness of fit is to 1, the better the compliance and the less the deviation.

[0033] Step 4.2, use the local diagnosis method to calculate the local diagnosis results; the local diagnosis method refers to focusing on key transitions, diagnosing the number of abnormal objects in the "missing and added" part of all objects consumed by the library required for the key transition, identifying and analyzing possible deviations in the process, mainly including two quantitative methods: consistency of abnormally enabled transitions and consistency of abnormally enabled transitions in event logs. The former is executed for a single process, and the latter is for the entire event log. The calculation methods are as follows: ; ; in, Execute for the process The local diagnosis results of the abnormally enabled transition consistency; Event log for object-centric business processes The local diagnosis results of the abnormally enabled transition consistency; yes Changes that are not enabled normally; It is a set of transitions that are not enabled normally; yes In process execution The number of tokens missing in the replay; yes In process execution The number of tokens consumed in the replay; Represents an object-centric business process event log Included The number of process executions corresponding to the log activity is calculated as follows: ; in, for Corresponding log activities; Will Figure 6 After the replay, the number of generated tokens 25, the number of consumed tokens 25, the number of missing tokens 2, and the number of remaining tokens 2 are substituted into the fitting formula and the result is Substituting into the fitting formula, the fitting result is 0.92; the local diagnosis result obtained by the method of the present invention includes two parts: the consistency of abnormally enabled transitions executed by a single process and the consistency of abnormally enabled transitions in the event log. Among them, the local diagnosis result of the consistency of abnormally enabled transitions executed by a single process is 0.5. However, the embodiment only includes one process execution, so the local diagnosis result of the consistency of abnormally enabled transitions in the event log is also 0.5, that is, when executing the transition PS, half of the objects are abnormal objects that do not exist in the previous set library and are additionally added, indicating that the degree of deviation of this activity in the actual execution of the process is relatively serious and should be checked and adjusted in time.

[0034] Based on the above method, the present invention designs an object-centric business process violation checking system based on token replay. The object-centric business process event log and the standard multi-object Petri net are input into the system, and the object-centric business process violation checking result is obtained as output, such as Figure 7 As shown, the system includes the following modules: The object graph acquisition and process execution module is used to acquire the object graph according to the dependencies between objects in the object-centric business process event log, and then obtain the process execution from the object graph using the connected component extraction method; The binding sequence acquisition module is used to delete cross-layer redundant dependencies in the process execution according to the transitive reduction method to obtain a simplified process execution, and then use the echelon partitioning mechanism to divide the simplified process execution to obtain a binding sequence; A module for obtaining replay results and deviation results is used to perform token replay on a multi-object Petri net model according to binding execution rules, and obtain replay results and deviation results in the violation inspection results of the object-centric business process; The object to be acquired is the central business process violation inspection result module, which is used to substitute the obtained replay results and deviation results into the defined fit formula to quantify compliance, and then use the local diagnosis method to display the part of the process with serious deviations. The object to be acquired is the fit results and local diagnosis results in the central business process violation inspection results.

[0035] The present invention also designs a storage medium storing a program, which, when executed by a processor, implements the above-mentioned object-centric business process violation checking method based on token replay and object-centric business process violation checking system based on token replay.

[0036] The present invention also provides a computing device, including a processor and a memory for storing a program executable by the processor, wherein when the processor executes the program stored in the memory, the object-centric business process violation checking method based on token replay and the object-centric business process violation checking system based on token replay are implemented. The computing device may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with a processor function.

[0037] The present invention provides a new solution to the problems that existing compliance checking technologies cannot accurately identify deviations and are difficult to confirm the source of deviations when processing object-centric business processes. It can accurately identify non-compliant behaviors and reduce the possibility of misjudgment or missed judgment, making the object-centric compliance checking technology more accurate. It also uses fitting metrics to quantify deviations and uses local diagnostic methods to display the severity of deviations in the execution of misfit processes, making it more adaptable and scalable. In addition, it can also be applied to the fields of machinery and equipment manufacturing processes, medical business processes, service processes, etc. It has practical promotion value and is worthy of promotion.

[0038] In order to prove the feasibility and superiority of the present invention, the following comparative experiment is given.

[0039] The comparative experiment uses the purchase and sales event log shown in Table 1. This process is an object-centric business process event log.

[0040] The method of the present invention is compared and analyzed with the existing flat token replay compliance checking technology. Figure 6 The multi-object Petri net of the standard process model of the purchase and sales scenario of the present invention is used, and the replay result is used as a comparison indicator. If the replay result can fully reflect the existing deviation, the compliance check method is better. Through token replay, the method of the present invention fully reflects the existing deviation. Table 4 is the event log after flattening by delivery order object type: Table 4 Event log after flattening by delivery order object type .

[0041] Figure 8 The multi-object Petri net of the invoice object type model is used. The existing flat token replay compliance checking technology is used to replay the log in Table 4 on the invoice object type model to obtain the binding sequence table of the invoice object type model as shown in Table 5: Table 5 Binding sequence table of the cargo list object type model .

[0042] Bind the sequence in Table 5 Figure 8 The token replay is performed on the multi-object Petri net, and the number of generated tokens is 3, the number of consumed tokens is 3, the number of missing tokens is 0, and the number of remaining tokens is 0; due to the missing inter-object dependency information, this method cannot detect that the commodity details object type is missing two X commodities when executing the PS activity. Therefore, the compliance check of the method of the present invention is more comprehensive and accurate. This proves the feasibility and superiority of the present invention.

[0043] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An object-centric business process violation detection method based on token replay, characterized in that: The steps include: Step 1: Obtain object-centric business process event logs, analyze dependencies between objects to construct object graphs, and then use connected component extraction methods to obtain process execution; Step 2: Use the transitive reduction method to remove cross-layer redundant dependencies, and then use the echelon partitioning mechanism to obtain the binding sequence; Step 3: Use the binding execution rules to perform token replay on the multi-object Petri net to obtain the replay result and deviation result; Step 4: Calculate the fitting result and the local diagnosis result to obtain the violation inspection result of the object-centered business process.

2. The object-centric business process violation checking method based on token replay according to claim 1, characterized in that: The specific process of step 1 is as follows: Step 1.1: The object obtained is the central business process event log It consists of a set of events representing operations, which include activities, occurrence time, end time and basic attributes of associated objects; Step 1.2: Construct an object-centric business process event log by analyzing the dependencies between objects The object graph is: ； in, It is an undirected graph node set consisting of all objects in the object-centric business process event log; It is an undirected graph edge set consisting of several pairs of objects with dependency relationships; The calculation formula is: ； in, , For events Two different objects are associated; The object represents all events in the central business process event log; Represents an object-centric business process event log Events All associated objects are calculated using the formula: ； in, Representation and Objects A linked sequence of events; Step 1.3: Use the connected component extraction method to obtain process execution; the specific process is as follows: First, the formal connected component extraction method is: ； in, is the connected component extraction method; represents the process execution obtained from the maximum connected subgraph; is the set of objects in the maximum connected subgraph; Then, according to The directed graph of the process execution is obtained. The definition is as follows: ； in, It is a directed graph node collection, connected by object-centric business process event log All events of any object in; It is a set of directed graph edges, consisting of pairs of events with direct following relationships; , The calculation formulas are: ； ； in, is an empty set; Represents an object-centric business process event log The direct follow-up relationship between events is calculated as follows: ； in, Different from the event events; For the events; For the events; For the an event.

3. The object-centric business process violation checking method based on token replay according to claim 2 is characterized in that: The specific process of step 2 is: Step 2.1, using the transitive reduction method to delete the cross-layer redundant dependencies in the object graph, and obtain a minimum subgraph that maintains the transitive closure characteristics of the original graph; the cross-layer redundant dependencies refer to the dependencies between nodes in the object graph realized by indirect paths, and maintaining the transitive closure characteristics of the original graph means that the reachability relationship between nodes in the graph remains unchanged after deleting the redundant dependencies; Step 2.2: Use the echelon division mechanism to divide the process execution; the specific process is: The no-predecessor node identification algorithm is iteratively implemented, and each time all the current no-predecessor nodes are obtained as an echelon, and the process execution is divided into several logically independent event echelons, which are disordered within the echelon and follow the established sequential logic between echelons; the no-predecessor node identification algorithm is to identify the nodes that currently have no predecessor nodes in the context of the graph and store them in a list; Step 2.3 Map each logically independent event echelon to its own binding to obtain the corresponding binding sequence; define the binding sequence as follows: ； in, For the A change, For the The specific objects of each object type consumed when a transition is executed; For the The binding corresponding to each transition.

4. The object-centric business process violation checking method based on token replay according to claim 3 is characterized in that: The specific process of step 3 is as follows: Step 3.1: Define a multi-object Petri net as follows: ； in, It is a Petri net; It is the mapping function from library to object type; is a set of variable arcs, is the set of all arcs in a multi-object Petri net; is the cardinality labeling function of the arc; and Represent the start mark and end mark respectively; The specific mapping relationship is: ； in, For the library collection, is a collection of object types; Step 3.2: Based on the binding sequence, the process execution is performed on the multi-object Petri net according to the binding execution rules to perform token replay, and the replay result and deviation result of each process execution are obtained; The replay result is the number of tokens generated, consumed, missing, and remaining after token replay for each process execution; Number of tokens generated Defined as: ； in, For Change The back set of the library is collected; The specific objects of each object type consumed when the transition is executed; It is a multi-object Petri net Token set of ; Token, which means the library Has an object ; Number of tokens consumed Defined as: ； in, For Change The front set of libraries is collected; Number of missing tokens Defined as: ； The remaining tokens Defined as: ； Deviation results include transitions that are not enabled normally during process execution and missing token information; The judgment rules for transitions that are not enabled normally are: when When binding In the logo Enable, change In the logo The following is the new identifier obtained by executing the enabled binding: , the calculation formula is: ； when When binding Unable to identify Enable, change For changes that are not normally enabled; Missing token information Defined as: ； Missing token information is a transition In the library The object required for binding to work properly is missing .

5. The object-centric business process violation checking method based on token replay according to claim 4 is characterized in that: The specific process of step 4 is as follows: Step 4.1, calculate the degree of fit, the formula is as follows: ； in, is the degree of fit; The object represents the number of process executions in the central business process event log; Indicates The number of tokens missing from a process execution in the replay; Indicates The number of tokens consumed by a process execution in replay; Indicates The number of tokens remaining in the replay for a process execution; Indicates The number of tokens generated by a process execution in a replay; Compliance is quantified based on the fit measure, and the closer the fit is to 1, the better the compliance and the less the deviation; Step 4.2: Use the local diagnosis method to calculate the local diagnosis result. The formula is: ； ； in, Execute for the process The local diagnosis results of the abnormally enabled transition consistency; Event log for object-centric business processes The local diagnosis results of the abnormally enabled transition consistency; yes Changes that are not enabled normally; It is a set of transitions that are not enabled normally; yes In process execution The number of tokens missing in the replay; yes In process execution The number of tokens consumed in the replay; Represents an object-centric business process event log Included The number of process executions corresponding to the log activity is calculated as follows: ； in, for Corresponding log activities; The replay results, deviation results, fit results and local diagnosis results are the required object-centric business process violation inspection results.

6. An object-centric business process violation checking system based on token replay, characterized in that: The object-centric business process violation checking method based on token replay as claimed in any one of claims 1 to 5 is adopted, the input of the system is the object-centric business process event log and the multi-object Petri net, and the output is the object-centric business process violation checking result; the system includes the following modules: The object graph acquisition and process execution module is used to acquire the object graph according to the dependencies between objects in the object-centric business process event log, and then obtain the process execution from the object graph using the connected component extraction method; The binding sequence acquisition module is used to delete cross-layer redundant dependencies in the process execution according to the transitive reduction method to obtain a simplified process execution, and then use the echelon partitioning mechanism to divide the simplified process execution to obtain a binding sequence; A module for obtaining replay results and deviation results is used to perform token replay on a multi-object Petri net model according to binding execution rules, and obtain replay results and deviation results in the violation inspection results of the object-centric business process; The object to be acquired is the central business process violation inspection result module, which is used to substitute the obtained replay results and deviation results into the defined fit formula to quantify compliance, and then use the local diagnosis method to display the part of the process with serious deviations. The object to be acquired is the fit results and local diagnosis results in the central business process violation inspection results.

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