122 results about "Process mining" patented technology

The invention relates to a business process standard dynamic optimization method and system based on a large language model, and belongs to the technical field of computer application, and the method comprises the following steps: constructing a data collection strategy template; performing anomaly detection on the key field based on a preset three-level anomaly detection mechanism to obtain standard structured data; constructing a process execution accurate quantitative evaluation model, and constructing a process topological graph by using a process mining technology; calculating an abnormal probability of each node in the process topological graph by using a multi-task graph neural network, and obtaining a high-attention area in the process topological graph based on the abnormal probability; analyzing the multi-source heterogeneous data associated with the high-attention area through a large language model, positioning root causes, calculating root cause influence weights, and obtaining a root cause list; establishing an intelligent bidirectional feedback mechanism to generate optimization suggestions of the standard process; according to the method, the problems of serious data redundancy, insufficient standard dynamics and lack of feedback mechanisms in the traditional flow standard optimization technology in the power industry are effectively solved.

The invention discloses an inductive process mining method and system based on local structure optimization. The method comprises the following steps: acquiring an event log executed by an actual business process; mining an original process tree model from the event log, searching and identifying an inaccurate structure in the original process tree model, and extracting a corresponding sub-log; iteratively optimizing the inaccurate structure in the sub-log to obtain a new sub-structure set; the new substructure set is combined by a Cartesian product to obtain a new substructure combination, a new substructure in the new substructure combination replaces a corresponding inaccurate structure in the original process tree model to obtain a result process tree model, the result process tree model is converted into a Petri net model, quality indexes fit and precision of the model are evaluated, a comprehensive quality index F-measure is calculated, and a final process tree model is obtained. And finally, selecting a Petri net model with the highest F-measure as output. According to the method, the model mining efficiency is improved, possible losses are reduced, and it is guaranteed that the high-quality structure is not affected while the inaccurate structure in the process model is improved.

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

The invention belongs to the technical field of data analysis and process mining, and particularly relates to a method for generating a parameterized interactive graphical process tree for business log data. The method comprises the following steps: firstly, collecting original business log data including database logs, application server logs, user behavior logs and the like, checking the integrity and timestamp accuracy of the original business log data, cleaning and standardizing the data, and extracting key process nodes; secondly, designing a tree data structure based on a DFG graph structure, defining attributes of nodes and connections, mapping data into the DFG structure to accurately represent the relationship between the nodes, and extracting key nodes; and then selecting a proper directed graph layout algorithm, optimizing node distribution, and distinguishing nodes by using visual elements. And finally, designing an interactive feedback system which comprises user interface design, specific service type screening and a real-time updating mechanism, so that the user can conveniently interact with the process tree, deeply analyze the service process and improve the understanding and optimization capability of the service process.

The invention discloses a real-time process mining method based on data analysis, which belongs to the technical field of data analysis, and comprises the following steps: providing a user interface, allowing a user to configure input parameters of a data acquisition task, including a target website URL (Uniform Resource Locator) and an acquisition frequency; the data acquisition module is used for realizing pre-definition and comprises a webpage crawler module which is used for automatically accessing and capturing contents of a target website and pre-processing the contents; an API calling module is provided and used for obtaining data from a third-party service or a data provider; the database query supporting module is used for directly extracting data from a database and carrying out encryption transmission on the data; monitoring the state and performance of the data acquisition task in real time; when abnormity or performance reduction is detected, the system automatically triggers an alarm; according to the method, the technical threshold of process mining is remarkably reduced through a user-friendly interface.

The invention discloses a collaborative monitoring system and method for a digital transformation process, and relates to the field of digital transformation monitoring, and the method comprises the steps: obtaining all data needing digital transformation; performing digital transformation according to an actual business process, analyzing a data relationship according to a preset process mining algorithm, constructing a business process model, and performing digital verification to generate a digital verification score; when the digital verification score is greater than a preset threshold value, verification is passed, and the collaboration degree between the activities is calculated; and performing anomaly analysis on the real-time data stream, when an anomaly is identified, marking the data of the activity of which the collaboration degree between the abnormal data and the activity is greater than a threshold value, and generating an early warning signal. According to the method, integration and multi-level association analysis of multi-source heterogeneous data are realized by constructing a direct association relationship matrix and a causal relationship set, and the accuracy and the flexibility of collaborative degree calculation among business activities are comprehensively improved by combining digital verification scores and a dynamic adaptation model.

A method is provided. The method is executed by an extraction engine implemented as a computer program within a computing environment. The extraction engine executes action and task mining on unstructured data. The method includes receiving a communication including unstructured data defining an action and automatically processing the communication by utilizing at least one generative artificial intelligence (AI) model to extract details of the action being performed in the unstructured data. The method includes automatically converting the action into an activity or a task of a process associated with the communication.

The embodiment of the invention relates to the field of data processing, and provides a financial institution business process intelligent optimization method and a related device, and the method comprises the steps: obtaining business process node data, business interaction support data and business result feedback data of a to-be-processed financial institution; based on a dynamic condition response mechanism, performing dynamic bottleneck process mining processing according to the business process node data and the business interaction support data to obtain bottleneck process analysis data; performing bottleneck process influence quantification processing according to the bottleneck process analysis data and the service result feedback data to obtain bottleneck process priority data; performing bottleneck process association rule mining processing according to the bottleneck process priority data, the business process node data and the business interaction support data to obtain bottleneck process association relationship data; according to the bottleneck process analysis data, the bottleneck process priority data and the bottleneck process association relationship data, business process optimization processing is carried out, and a more accurate business process optimization strategy can be obtained.

The large-model-based data security risk automatic research and judgment processing system comprises a fusion research and judgment module which is used for carrying out comprehensive research and judgment on the risk level, attack intention and potential influence of a security event based on security data in combination with deep learning and a large-scale language model, and transmitting a research and judgment conclusion to an automatic processing module; the business process disassembling and analyzing module is used for automatically discovering and modeling based on the security data through process mining and flow analysis, perceiving a business process and a dependency graph associated with a security event in real time, evaluating potential influences of different disposal measures on business continuity, and transmitting business influence evaluation to the automatic disposal module; and the automatic disposal module is used for selecting and executing an optimal risk disposal strategy from the security strategy library by adopting a distributed architecture of an AI intelligent agent according to the research and judgment conclusion and the business influence evaluation. The accuracy of alarm study and judgment is improved, 'alarm fatigue 'is relieved, and the method has high elasticity, high fault tolerance and strong cooperative capability.

Documents and other process area events in an Enterprise Resource Planning (ERP) system are extracted according to a knowledge table. The extracted events are stitched together in a timeline order, placed in a transport container (or otherwise transmitted), and input to, for example, a process mining or process intelligence tool. The tool utilizes any of AI, Machine learning, or other techniques to evaluate processes and / or documents represented by the extracted events, and triggering alerts or messages based on the evaluation. The tool may be a task mining tool and / or provide information or suggestions for improving the document or process represented by the document(s) and may prepare and display (or provide for display) a visual model of the evaluated process or document and may highlight anomalies therein.

The invention provides a dynamic data desensitization system based on a federated learning technology, and the system comprises a dynamic desensitization engine which is used for calculating a desensitization strategy weight according to a strategy weight functional expression, selecting a desensitization strategy matched with the desensitization strategy weight, and carrying out the desensitization processing of data; the federated flow mining module is used for calculating flow model parameters, uploading the flow model parameters to the federated flow mining module, extracting flow model features of a department, sharing feature vectors, and ensuring that the shared features cannot be reversely pushed back to original log data according to a differential privacy noise control algorithm; the block chain auditing chain is used for writing the hash value into the block chain to form an operation fingerprint and sending alarm information according to the operation fingerprint; and the RPA compliance robot is used for resetting the account permission, backtracking the influenced data copy, performing desensitization processing again and sending an investigation processing notification to compliance personnel.

The invention belongs to the field of industrial control safety, provides a PLC (programmable logic controller) memory behavior analysis-based control system configuration logic anomaly detection method, and solves the problems of logic attack detection blind areas, incomplete detection coverage, process semantic deficiency and the like caused by hidden attacks (modifying PLC control logic through a legal programming interface). The method comprises the following steps: S1, collecting a PLC register with a timestamp and a coil state sequence; s2, identifying a key memory address, constructing an address cluster set, and supporting event completion and process mining; s3, generating an XES standard event log by adopting a clustering and symbol back-pushing method; s4, improving a Heuristics Miner algorithm, automatically identifying a starting point and an end point of a control flow, and constructing a dependency graph with an identifier; s5, converting the control logic into a formalized model, and defining a legal state transition sequence; and S6, monitoring a real-time event stream on line, mapping the real-time event stream to the model and dynamically playing back the real-time event stream, and realizing anomaly detection by combining track alignment and comparison. The method is complete in coverage, clear in semantics, capable of tracing attack paths and capable of improving the defense capability of a logic layer.

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.

The invention relates to the technical field of data credibility evaluation, and discloses a whole-process flow mining data credibility evaluation system, which comprises a processor used for controlling the operation of the system; the data source credibility evaluation module is used for objectively evaluating the credibility of the data source; the data quality evaluation module is used for evaluating the consistency, integrity, accuracy and the like of the data by using objective data quality evaluation indexes; the data cleaning and preprocessing module is used for cleaning and preprocessing the mining data according to the evaluation result; and the data mining algorithm module is used for mining the processed data by using a data mining algorithm. Through objective data quality evaluation indexes, the consistency, integrity, accuracy and the like of the data are evaluated, and the influence of subjective judgment is reduced. And different judgments on the quality and credibility of the data due to subjective judgments of the user are avoided, so that the certainty of a system evaluation result is improved.

Methods and systems for process analysis include determining attributes of an event log relating to a process. Attributes of an end task of the process are determined based on a comparison of a vector representation of the end task to vector representations to a set of criteria relating to process representations. Hyper-parameters of process mining models are tuned based on the attributes of the event log and the attributes of the end task. The process mining models are ranked based on the attributes of the event log and the attributes of the end task. The event log is mined using a top-ranked process mining model to generate a process representation. An inefficiency of the process is identified based on the process representation. The inefficiency is automatically corrected.

The invention discloses a business process log case attribute automatic identification method and system, and belongs to the field of process mining, and the method comprises the following steps: extracting features from an event log; a binary classifier is constructed, the probability that each column belongs to the key attributes is output, and N attributes with the highest column probability are selected to form a candidate attribute set of each column; judging whether the candidate attribute set of each column is a single candidate attribute or a candidate attribute combination; combining the candidate attributes, and calculating the score of each candidate attribute combination; and selecting the candidate attribute combination with the highest score as final key attribute output. The system comprises an event log acquisition and feature extraction module, a candidate key attribute range narrowing module, a candidate attribute set judgment module, a process model discovery and evaluation module and an optimal candidate combination selection module. According to the method, the case attributes in the event logs can be automatically recognized for the event logs without labels, and the requirement for manual appointing is reduced.

The invention provides a business workflow dynamic arrangement and optimization method based on a large model agent, and the method comprises the steps: (1) analyzing the features and operation logic of a multi-element business workflow, and defining a business workflow description language; and (2) decomposing a complex business process construction process, and providing a layered process modeling system to guide a large model agent to construct a workflow model. And (3) constructing a task execution queue based on the task dependency relationship, and dynamically executing and monitoring the workflow. And (4) according to the execution log of the business workflow, discovering the bottleneck, redundancy and abnormity of the workflow model by adopting a process mining algorithm, and proposing a process improvement and optimization strategy. And (5) constructing a collaboration framework of a plurality of large model agents, and realizing dynamic arrangement, monitoring and optimization of the business workflow. According to the method, the defects that the current business workflow construction depends on experience of designers, and effectiveness and function requirements are difficult to guarantee are overcome; the defect that the business workflow is difficult to adapt to dynamic demand changes is overcome.

The invention discloses an AI business process automatic generation method based on AI model combinatorial optimization, and the method comprises the following steps: constructing a multi-level AI capability decoupling and reconstruction module, and carrying out the bottom-up hierarchical modeling and top-down modular decoupling, dynamic mapping of an AI atomic power layer, an AI modular production capacity layer, an AI general capability layer and an AI application business layer is realized; an elastic AI capability combinatorial optimization module is constructed, AI capability combinatorial optimization oriented to three dimensions of data, features and models is carried out based on service quality requirements, and multiplexing, combination and arrangement of AI capabilities are realized through evolutionary computation optimization driven by an agent model; and an AI business process automatic generation module is constructed, an AI service containerization deployment scheme is generated and optimized based on data-driven process mining and a hyper-heuristic algorithm, and AI business process automatic generation is realized. According to the invention, the adaptability and execution efficiency of the AI technology in a complex scene can be improved.

A method, computer system, and a computer program product for process mining and optimization is provided. The present invention may include identifying a process to be analyzed, wherein the process is comprised of one or more activities. The present invention my include generating a list of relevant individuals corresponding to the process and requesting access to collaborative communications between one or more individuals from the list of relevant individuals. The present invention may include generating one or more scores based on the collaborative communications between the one or more individuals. The present invention may include updating the process within the user interface using the one or more scores to enrich the process with additional contextual data.

The invention discloses a cross-organization emergency response process model extraction method based on a large language model, belongs to the field of process mining and natural language processing, and constructs a large language model general prompt framework containing emergency response process element definition and judgment criteria. Based on a general prompt framework, a prompt strategy fusing role playing and context learning is adopted to extract explicit entities such as emergency organizations and response tasks and relationships of the explicit entities. Inferring and extracting implicit entities and implicit relation elements on the basis of explicit elements by adopting a thinking chain fused prompt strategy; and finally, making a mapping rule to convert the extracted complete process elements into a logically coherent cross-organization emergency response process model, and presenting a result in a graphical manner. According to the method, domain data training is not needed, accurate flow element extraction can be achieved under the condition of few samples, the problem of implicit flow element missing is solved, and technical support is provided for emergency decision making.

The present invention discloses a multi - perspective process mining method and system for supporting role - activity relationship recognition, including: 1) obtaining the event log of the business process; 2) role recognition in the business process event log; 3) clustering the activities included in the business process event log, and performing role - activity relationship recognition on roles and activities; 4) mining the business process model, and annotating the process model according to the recognized role - activity relationship; 5) visualizing the annotated business process model containing role - activity relationships in the form of a swimlane diagram and annotating roles in the model to obtain a multi - perspective process model for supporting role - activity relationship recognition. The present invention breaks through the limitation of traditional business process mining in modeling and mining business process models from the perspective of activities, and adds role information to the mining of business process models, thereby more conveniently describing business processes and intuitively describing the logical relationships between various activities in business processes.

The invention discloses an aviation task execution efficiency analysis method based on process mining, which comprises the following steps: acquiring task execution data from a plurality of aviation business systems, and constructing an initial event log, executing data standardization processing and generating a standardized event log according to four serial links of demonstration, planning, monitoring and redisk; performing flow mining and efficiency quantitative modeling based on a standardized event log, forming a feature vector according to the duration of each task case in four links to obtain a flow model, obtaining an efficiency grade division result by adopting a clustering algorithm, and obtaining a processing efficiency reference value; based on the efficiency grade division result and the processing efficiency reference value, performing real-time anomaly detection on the execution data of the newly-added task case, calculating a process model of the newly-added task case, and obtaining a deviation degree of a clustering center corresponding to the efficiency grade division result and / or a deviation degree with the processing efficiency reference value; and in response to early warning triggered by abnormal detection, positioning a flow bottleneck to implement an optimization measure.

The present disclosure provides a method for optimizing a cross-system permission approval path and related devices, the method comprising: obtaining a historical approval log, and constructing an approval process model based on a process mining algorithm; calculating a comprehensive risk value for a permission application based on a risk assessment algorithm; and generating an approval path based on the approval process model and the comprehensive risk value corresponding to the permission application. The present disclosure constructs a dynamic approval path through process mining technology, and combines a multi-dimensional risk quantification model to achieve dynamic generation of the approval process, thereby significantly improving the efficiency of permission approval.

The application provides a procurement document generation and evaluation method based on process mining analysis, relates to the technical field of enterprise management, and comprises the following steps: collecting event log data in an enterprise procurement system, constructing a procurement process hypergraph model through a process hypergraph discovery algorithm, and generating a variation correlation topological structure; constructing a self-adaptive rule mapping network according to the procurement process hypergraph model and the variation correlation topological structure, inputting specific procurement demand data into the evolved template by utilizing the network to dynamically evolve the procurement document template, filling the file content by applying a semantic intervention filling mechanism, and generating a complete procurement document; the process hypergraph discovery algorithm is introduced to construct the procurement process hypergraph model, so that complex correlations such as multi-party interaction, group decision and high-order dependence in the procurement activity can be effectively captured, and the expression capacity of the process model is significantly improved compared with the traditional Petri net or simple directed graph model.

The present invention discloses a method, device and storage medium for simplifying a flow chart, and belongs to the technical field of process mining. The method comprises obtaining nodes extracted in advance from a process log to be simplified; sending each node to a pre-trained classifier for prediction to obtain a prediction result; judging whether it is a key node according to the prediction result, and if so, retaining it in the process log, and saving the previous non-key node as a child node of the node; if not, marking it as a non-key node; applying a process mining algorithm to the simplified process log to obtain a simplified flow chart; the present invention can effectively reduce the nodes in the flow chart, while retaining the original semantics, and has a good practical application effect.

The present application belongs to the field of computer data processing, and particularly relates to a process bottleneck intelligent attribution method and system fusing multi-dimensional risks and emergencies, aiming to solve the problems that traditional process mining cannot explain bottleneck causes and that risk and process efficiency analysis is fragmented. The present application comprises: generating a fusion data set by fusing process execution data and multi-dimensional risk data; calculating performance indicators of each link based on the process execution data, identifying bottleneck links; monitoring the performance indicators of the bottleneck links, detecting emergencies and generating an event feature vector, extracting corresponding multi-dimensional risk data from the fusion data set to construct a risk vector; inputting the risk vector and the event feature vector into an attribution model to determine the quantitative contribution of each input feature to the performance indicators of the bottleneck links, which is output as an attribution result. The present application can improve business approval decision efficiency and accuracy, and enhance dynamic risk identification and response capabilities.

The present invention relates to a computer-implemented method to generate a process mining repository for analyzing process data, wherein the process data is a multidimensional large-scale dataset which is extracted from at least one external computer system and transformed into a number of data models. The process mining repository represents a process workspace, in which the user may conduct process mining on a valid data model, i.e., explore process data, for instance, using a dynamic question and answer framework.

The application discloses a medical path accurate classification model generation method and system based on case feature labels, first extracts medical logs into structured logs, and collects treatment event sequences of each case; then, similar cases are aggregated through trajectory clustering, process mining is carried out on each clustering cluster, a process tree model is generated and a cycle node is optimized, and a medical path is extracted from the process tree model; a trained neural network model cluster is used to determine the case feature labels of the extracted medical path; finally, the case feature label determination and the corresponding medical path are integrated to form a usable medical path accurate classification model. The application combines trajectory clustering, process discovery, neural network and other technologies, fully gives play to the advantages of each technology, and the performance of the obtained model is much higher than that of a model generated by a single technology, so that the purpose of accurately recommending a medical path according to case features can be achieved.

The embodiment of the invention provides a customer service standard operation process mining method, an intelligent dialogue method, electronic equipment, a storage medium and a computer program product. The customer service standard operation process mining method comprises the steps of performing information extraction and aggregation processing on service trajectory data of historical customer service dialogues to obtain a set of service trajectory data corresponding to consultation questions in different customer service scenes; according to the time sequence of customer service operation in the service track data, frequent customer service operation path sequence mining is carried out on the service track data in the set, and target service paths associated with various consultation questions in different customer service scenes are obtained; and based on the target service path, generating customer service standard operation process trees corresponding to various consultation questions in different customer service scenes. According to the method, the acquisition efficiency of the customer service standard operation process is improved.

The present invention claims protection for a method, system, and medium for process mining consistency checking based on task types, belonging to the field of data mining, and a method for evaluating the structure of a process mining model. The present invention is a consistency checking method that compares the mining model with the original model and combines task types. First, traverse the original model and the mining model to obtain the total number of tasks, visible tasks, repeated tasks, and invisible tasks in each model, then calculate the proportions of repeated tasks and invisible tasks in the model, and finally substitute them into the structural evaluation formula for calculation. The invention can effectively perform consistency checking on the mining model, thereby reasonably evaluating the structure of the model to achieve the purpose of evaluating the effectiveness of the mining algorithm.