76 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.

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.

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 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.

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 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 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 application discloses a kind of multi-modal business process chart generation method, system and computer equipment, the method includes: obtaining recording data, screen recording data and event log containing user number and time stamp attribute;Segmented log representing a business step and its start time and end time are obtained by segmenting and merging event log;According to the start time, end time of each business step, extract the recording segment from recording data and generate word cloud, extract screen keyword from screen recording data and generate word cloud, extract operation sequence from the event log of business step, obtain the operation sequence, recording word cloud, screen word cloud representing business semantics;Based on the segmented and merged event log, apply process mining algorithm, and increase corresponding operation sequence, recording word cloud, screen word cloud, obtain multi-modal business process chart.The application can simultaneously process multi-modal data of recording, screen recording and event log, and generate multi-modal business process chart.

This invention discloses a method and system for predicting the remaining time of a business process based on a hybrid architecture, belonging to the fields of artificial intelligence and process mining. The method includes: acquiring the original event logs of the business process to generate a set of event sequence prefixes to be predicted; introducing position encoding vectors and time difference vectors into an event embedding model; using a multi-scale one-dimensional convolutional neural network for multi-scale local feature representation; introducing a weight decay factor based on the time difference between events into the attention calculation of the time-aware Transformer encoder; and aggregating and predicting the remaining time of the business process at the fully connected output layer. The system includes an event prefix acquisition module, an embedding and position encoding module, a convolutional feature extraction module, a Transformer encoding module, and a remaining time prediction module. This invention integrates event attributes, multi-scale local patterns, and global temporal dependencies, significantly improving prediction accuracy and model robustness.

The invention relates to the technical field of business process mining, and provides a dynamic extensible multi-view business process event extraction method, system and device and a medium, and the method comprises the steps: firstly splitting an original application log into basic event streams and event detail archiving data, then determining an object, an object attribute and an event attribute corresponding to an event, screening events from different object perspectives and extracting attribute values based on an event-object process model, integrating events of the same object instance to form a track according to the principle that the object attribute values are the same, combining the track by utilizing object association, time window overlapping and event similarity, and generating a business process track. And finally, combining the event attribute value and the business process track to generate an event log and outputting the event log in an XES format. The business process event can be efficiently extracted from the complex log, the generated XES format log is convenient for subsequent analysis and processing, and the intelligence and refinement level of business process management is improved.

The invention relates to the technical field of low-code platform business processes, in particular to a business process platform based on low-code and process mining fusion, and the platform comprises an event receiving module which is used for receiving an initial business event; the instance reconstruction module is used for generating a complete business process instance to be detected; the custom event detection module is used for detecting whether a custom business event based on low codes exists in the instance or not; the logic consistency verification module is used for sequentially executing full composition verification, internal logic verification and logic verification before and after the event when the user-defined business event exists; and the executive evaluation module is used for performing executive evaluation on the custom business event and feeding back a result to the event receiving module. According to the method, through fusion of low-code development and process mining, automatic reconstruction of business process instances, intelligent detection of custom events, multi-layer logic consistency verification and executive automatic feedback are realized, and high efficiency and intelligence of process management are realized.

The digital dual-cycle task management system for enterprise operations, comprising a process sorting module, a digital remodeling module, and an empowering operation module. The scheme can establish a dual-cycle task management system, taking the strategic goals of enterprises as the starting point, and combining the status of digital operation management of various enterprises to make targeted resource investments and improvements, thereby achieving optimal resource allocation; the scheme employs process mining algorithms to comprehensively sort and optimize business processes, breaks down corporate strategic goals into different business areas to achieve the visualization of sub-goal business processes, thereby enhancing the operational efficiency of enterprises and reducing costs; the scheme utilizes automated analysis to finely manage data generated by process operations, clarifies the behavioral trajectories, waste, inefficiencies, and problem points in different business departments through data support, thereby enabling targeted training and assisting personnel in enhancing their effectiveness.

The invention discloses an online activity prediction method fusing process constraints, and relates to the technical field of process mining, and the method comprises the steps: collecting a process execution log of a historical event, extracting an initial track list from the log, and forming a training sample pair by a prefix subsequence of each track in the track list and a next activity corresponding to the prefix subsequence; pre-training an activity prediction model by the training sample pair, screening a completed track list from the initial track list, and extracting a process model from the completed track list by using a process discovery algorithm; if the flow of the new event has concept drift, triggering online updating of the activity prediction model and the flow model; and for each moving track prefix on the new event, generating a next candidate activity by using the activity prediction model, performing constraint screening in combination with the process model, and outputting the activity with the highest prediction probability. According to the method, the problem that prediction and drift detection are lack of collaboration in an existing online activity prediction method is solved.

The disclosed system and methods relate to guided process mining. A system includes a processor and memory configured to provide a graphical user interface to a user device. The interface includes a user-selectable-parameter element and representations of processes. Upon user selection of a process, a guided investigation is launched based on the current setting of the user-selectable-parameter element. Upon completion of the investigation, a second graphical user interface is provided, configured to present data regarding the process based on user interactions during the investigation. The system also includes methods for process mining using integrated data from multiple systems, and for generating templated objects for process mining.

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 disclosed system and methods relate to guided process mining. A system includes a processor and memory configured to provide a graphical user interface to a user device. The interface includes a user-selectable-parameter element and representations of processes. Upon user selection of a process, a guided investigation is launched based on the current setting of the user-selectable-parameter element. Upon completion of the investigation, a second graphical user interface is provided, configured to present data regarding the process based on user interactions during the investigation. The system also includes methods for process mining using integrated data from multiple systems, and for generating templated objects for process mining.

A process data store may contain a process model (e.g., a process graph, with process graph elements that include nodes and edges, as generated via process mining or a BPMN representation). A process server may retrieve information from the process data store and receive user feedback data. The server may determine if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store. If the information retrieved from the process data store is not associated with a prior mapping of survey questions, embodiments may utilize Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store. The server may then automatically assign, group, and analyze the user feedback data to generate a recommended alteration.

The invention relates to the technical field of enterprise management, and particularly discloses a digital dual-cycle task management system for enterprise operation, which comprises a process carding module, a digital remodeling module and an operation enabling module. According to the scheme, a dual-cycle task management system is constructed, the strategic target of an enterprise is taken as a starting point, the digital operation management condition of each enterprise is combined, resource investment and improvement are performed in a targeted manner, and optimization of resource configuration is achieved; the business process is comprehensively sorted and optimized by using a process mining algorithm, and enterprise strategic targets are implemented to different business fields through target disassembly, so that visualization of the sub-target business process is realized, the enterprise operation efficiency is improved, and the cost is reduced; through automatic analysis, fine management is performed on data generated by flow operation, and tasks are automatically triggered to be allocated to corresponding business departments, so that targeted cultivation is realized, and personnel are helped to improve efficiency.

The invention discloses an electric power material supply chain event reconstruction method and device based on process mining. The method comprises the steps of obtaining electric power material supply chain log data; performing flow fitting on the log data of the electric power material supply chain to form a fitting circulation supply chain including each supply chain event node; extracting a node time feature of each supply chain event node in the fitting circulation supply chain, analyzing an abnormal condition of the node time feature, and giving a target supply chain event node; and based on a plurality of process analysis dimensions, carrying out process variant analysis on the basis of a target supply chain event node, giving a supply chain abnormal path and a corresponding dimension label, carrying out contribution degree analysis based on the supply chain abnormal path and the corresponding dimension label, identifying an electric power material supply chain abnormal event, and completing event reconstruction of the electric power material supply chain. Accurate positioning of abnormal conditions in the electric power material supply chain is achieved, abnormal events are reconstructed, the problem handling efficiency of the electric power material supply chain is improved, and collaboration among all links is guaranteed.

In some implementations, a system may receive an event log including event data associated with a plurality of events that were performed by one or more external devices including the IoT device. Accordingly, the system may parse the event log to identify activity data, from the event data, associated with at least one activity that includes one or more events of the plurality of events. The system may further generate a case associated with the at least one activity and including information generated based on the activity data and may perform a process mining operation on the case including the activity data.

The present disclosure generally relates to the field of data structures and in particular, a loop-aware footprint matrix data structure adapted for data process traversal. The proposed approach is directed to a computer-based analytic system and corresponding method that uses a specific data structure and processing thereof, in some embodiments, adapted to computationally estimate predictions of next events by first generating a data structure based on business process models obtained using process mining techniques, and then using the improved data structure for generating predictions, which can then be encapsulated in the form of computer instructions or machine instruction sets, having a specific sequence for execution.