38 results about "Process mining" patented technology

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 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 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 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 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.

The application discloses a kind of inductive process mining optimization method and system based on multi-agent cooperation.First, the low-frequency trajectory variant in event log is denoised and frequency is counted, and an inductive mining algorithm is used to mine a process tree model, an external knowledge base is retrieved in the context of business process, and structured information input is constructed.Second, a multi-agent group collaboration mechanism of "generation-review-optimization" is introduced, wherein the initial model generation group generates an initial process model based on structured information input, the expert review group reviews the initial process model from three dimensions of process tree operator, model structure robustness and activity event completeness, and the model optimization group refines and optimizes the model based on expert review.The method can effectively handle event logs with high complexity, noise interference and a large number of low-frequency behavior variants, understand the context information of business processes, and generate a process model with reasonable structure and accurate behavior.

The invention discloses an internal control compliance review method and system based on process mining, and relates to the technical field of intelligent management.The method comprises the steps that a process chain constructed based on an event sequence is extracted from a process event table, and interference events are input; performing interference assignment on each flow node in the flow chain according to the interference event to obtain a flow interference information entropy, and outputting the flow interference information entropy as a first compliance review result; reconstructing the process chain according to the interference event to obtain a reconstructed process chain, and performing compliance risk index analysis to obtain a second compliance review result; and outputting a compliance review result according to the first compliance review result and the second compliance review result. The technical problems that an existing compliance review method depends on static analysis and manual auditing, the efficiency is low, and interference events and risks thereof in a complex process cannot be accurately recognized are solved, and the technical effects that the influence of the interference events is quantified based on process mining, process reconstruction optimization is conducted, and the review efficiency and accuracy are improved are achieved.

Computer systems, apparatuses, processors, and non-transitory computer-readable storage devices configured for executing a method comprising performing process mining on user log data; translating results of the process mining into actionable insights using a machine learning engine; and providing an option to convert the actionable insights into one or more tickets for a product management team.

The embodiment of the invention relates to the field of data processing, and provides a business process optimization strategy generation device and method of a financial institution, a terminal and a medium, 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 embodiment of the invention provides an information system performance auditing method based on process mining and related equipment, and belongs to the technical field of information system auditing. The method comprises the following steps: acquiring a running log and standard business process information of an information system; preprocessing the log to obtain a structured event log; utilizing a process mining technology to construct an actual operation process model from the event log; and performing consistency comparison on the actual model and the standard model, calculating a business satisfaction index, and realizing quantitative evaluation of the business process supporting capability. Further, the method can also analyze access time distribution, total fluctuation trend and cross-domain call dependency in the log data, generate multi-dimensional performance indicators such as utilization rate, adaptability and synergy, and automatically identify and audit doubtful points. The process mining technology is creatively applied to the field of performance auditing, automatic, intelligent and multi-dimensional auditing based on objective data is achieved, and the auditing accuracy, depth and efficiency are remarkably improved.

The present invention relates to a method to generate an object-centric data model for process mining from any external computer system for any process, wherein the method reduces the cognitive challenge for creating the object-centric data model through queries on raw process data stored in the external computer system.

The invention discloses a trusted cross-organization process mining method and system based on a block chain, and the method comprises the steps: filtering an original event log, and obtaining an interaction event log; private and public process models are mined by using the original and interactive event logs; the public process model after block chain smart contract verification is linked and synchronized among the nodes; any organization submits the merging operation as a transaction to a block chain, and the block chain is driven by a smart contract to fuse the public process model of each collaborative organization into a global public process model and synchronize the global public process model; any organization submits the simplified operation as a transaction to a block chain, the block chain is driven by a smart contract, and public process model fragments related to the own side and the own side related collaborative organization are obtained and synchronized according to global public model simplification; and integrating the public process model fragment and the own private process model under a block chain to generate a cross-organization collaboration process model from a local organization perspective. According to the invention, mining and optimization of cross-organization business processes are facilitated.

The application provides a process mining-based anomaly discovery method and device, comprising: obtaining to-be-processed log data, wherein the to-be-processed log data at least comprises to-be-mined log data and to-be-detected log data; obtaining a process model according to the to-be-mined log data; sampling the to-be-detected log data to obtain a sampling result; calculating a cost cost according to the sampling result and the process model by using a cost function; the cost function comprises a transition cost function and an event cost function; performing process consistency detection on the to-be-processed log data based on beam search according to the cost function, the process model and the cost cost to obtain a target alignment scheme, and discovering anomalies of the to-be-processed log data according to the target alignment scheme. The application accelerates process consistency detection by using the method of beam search, and improves the accuracy of alignment calculation by using the cost function, so that the result is more accurate while being accelerated.

The application discloses a block structure process mining method for incomplete event logs caused by concurrency, takes an event log described in XES format as input, and takes a process model file described in PTML format as output result; firstly, an Inductive Miner-like method is used to split the incomplete event log to determine different block structures and the nesting relationship therebetween, and further processing is needed for an incomplete sub-log caused by concurrency which cannot be correctly split; secondly, a collaborative filtering is used to infer a possible missing transitive precedence for the sub-log, and a split operation applied to a transitive precedence graph is used for split operation; finally, all block structures are combined into a block structure process model represented by a process tree according to the nesting relationship. The application can process the incomplete event log caused by concurrency, and mines a process model close to an actual process model as much as possible.

The application relates to the technical field of enterprise management, and particularly discloses a digital double-cycle task management system for enterprise operation, which comprises a process analysis module, a digital remodeling module and an operation empowerment module. The double-cycle task management system is constructed, the strategic target of the enterprise is taken as a starting point, the conditions of digital operation management of various enterprises are combined, resource investment and improvement are carried out in a targeted manner, and the optimization of resource allocation is achieved; a process mining algorithm is used to comprehensively analyze and optimize the business process, the enterprise strategic target is implemented to different business fields through target decomposition, the visualization of the sub-target business process is realized, the efficiency of enterprise operation is improved, and the cost is reduced; through automatic analysis, the data generated in the process operation is finely managed, the task distribution to the corresponding business department is automatically triggered, targeted cultivation is realized, and the efficiency of personnel is improved.

The invention discloses an unmanned vehicle operation log completeness detection method based on a standard process, and the method comprises the steps: generating a simulation operation log based on a preset unmanned vehicle standard business process, carrying out the structural analysis and activity hierarchy abstraction of the simulation operation log, and constructing a standard event log; constructing a standard process model by adopting a process mining algorithm based on the standard event log, and extracting a standard activity set and a standard path set; and converting the real operation log into a real event log, performing alignment analysis on the real event log and the standard process model, calculating a process completeness index, and outputting missing process activity information and process path information for realizing quantitative detection of the process completeness of the operation log of the unmanned vehicle.

The invention discloses an intelligent manufacturing optimization method and system based on process mining and lean digital twinning, and belongs to the technical field of computer application and industrial manufacturing. The method comprises the following steps: extracting real-time production data from a heterogeneous data source and converting the real-time production data into a process event log; analyzing the process event log through a process mining engine, and constructing a dynamic value flow digital twinborn body; performing lean state diagnosis on the process event log on the dynamic value flow digital twinborn body to generate a corresponding diagnosis result; based on the diagnosis result, taking the dynamic value flow digital twin as a simulation base, performing hypothesis analysis through a simulation engine, predicting the influence of different improvement measures on the key performance indicators, generating an optimization strategy, and pushing the optimization strategy to a production execution system; and on the basis of a newly generated process event log after the optimization strategy is executed, repeating automatic discovery and construction of the dynamic value flow digital twin and subsequent real-time diagnosis steps so as to carry out continuous verification and iterative optimization on an optimization effect.

The invention provides a purchase file generation and evaluation method based on process mining analysis, and relates to the technical field of enterprise management, and the method comprises the steps: collecting event log data in an enterprise purchase system, constructing a purchase process hypergraph model through a process hypergraph discovery algorithm, and generating a variation association topological structure; constructing an adaptive rule mapping network according to the purchasing process hypergraph model and the variation association topological structure, inputting specific purchasing demand data into an evolved template by utilizing a network dynamic evolution purchasing file template, and filling file contents by applying a semantic intervention filling mechanism to generate a complete purchasing file; according to the method, the purchase process hypergraph model is constructed by introducing the process hypergraph discovery algorithm, complex associations such as multi-party interaction, group decision and high-order dependence in purchase activities can be effectively captured, and the expression ability of the process model is remarkably improved compared with a traditional Petri net or a simple directed graph model.

The invention belongs to the field of computer data processing, particularly relates to a process bottleneck intelligent attribution method and system fusing multi-dimensional risks and emergencies, and aims to solve the problems that traditional process mining cannot explain bottleneck causes and risk and process efficiency analysis is separated. The method comprises the following steps: fusing process execution data and multi-dimensional risk data to generate a fused data set; calculating performance indexes of each link based on the process execution data, and identifying a bottleneck link; monitoring bottleneck link performance indexes, detecting emergencies and generating event feature vectors, and extracting corresponding multi-dimensional risk data from the fusion data set to construct risk vectors; and inputting the risk vector and the event feature vector into an attribution model, determining a quantitative contribution degree of each input feature to a bottleneck link performance index, and outputting the quantitative contribution degree as an attribution result. According to the invention, the business approval decision-making efficiency and precision can be improved, and the dynamic risk identification and coping capability is enhanced.

The application discloses a kind of business process mining method, device, processor and electronic equipment. It is related to artificial intelligence field, wherein, the method comprises: obtaining the multi-modal data of business process, wherein, business process includes N links, N is the positive integer greater than 1, multi-modal data includes at least two of the following data types: business log, business audio data, business video data;According to multi-modal data, determine the N groups of link data corresponding to N links respectively, wherein, each group of data in N groups of link data is part of the data contained in multi-modal data;According to N groups of link data, determine the business behavior included in business process, and determine the behavior characteristics of business behavior respectively;According to behavior characteristics, determine the behavior to be optimized from business behavior. Through the present application, the technical problem that it is difficult to mine the problem behavior in the process in the process of the flow of business is solved.

The invention provides a dynamic performance test method and system based on business portraits and intelligent analysis, and belongs to the field of business system application in the building industry. According to the technical scheme, a service portrait is constructed based on multi-source data of a service system; forming a business-performance heterogeneous dynamic graph based on the business portrait; deducing a propagation path of the risk along the dependency relationship; acquiring a test result according to the performance risk score and the propagation path; and recharging a test result to the business portrait and the business-performance heterogeneous dynamic graph for calibration and updating. The method has the beneficial effects that a service portrait is constructed through service event standardization and process mining, a service-performance heterogeneous dynamic graph is established, risk prediction and propagation path inference are intelligently realized based on the graph, a risk-driven dynamic pressure measurement scene and adaptive load distribution are generated, online safety control and result recharge closed-loop calibration are matched, and the service performance is improved. And interpretable, reproducible and continuous optimization of the performance test consistent with real business semantics and dependency structures is realized.

The invention discloses an electric power procurement whole-process data tracing device and method based on an internal mining special area, and a medium, and relates to the technical field of electric power procurement data processing. The method comprises the steps of building multi-source event collection, event normalization and business chain aggregation, evidence object generation and binding, compliance constraint modeling, online compliance verification, key gate verification, conflict detection and correction, and compliance proof packet generation and playback. A full-process data tracing mechanism including process mining, illegal mode library updating, illegal mode library recharging and strategy iteration is adopted, so that five-dimensional linkage of states, facts, evidences, rules and time is realized; the problem that in the prior art, due to the facts that the process state is separated from business facts, evidence materials are unhooked from time nodes, and no verifiable basis is provided for exceptional processing, the compliance chain is broken is fundamentally solved, and auditing, traceable and self-certified compliance guarantee capacity is provided for the whole process of electric power purchase.