953 results about "Business process" patented technology

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the technical field of software testing, and discloses an automatic test script generation method and system, a storage medium and electronic device.The method comprises the steps that test case information and tested environment information are received, and structured case representation is generated; enhancing the structured use case representation based on a preset knowledge base; generating a plurality of positioning candidates for the target control in the testing step, and establishing a priority relationship among the positioning candidates; generating an executable automatic test script; running the script, and when execution failure is detected, automatically adjusting a positioning strategy, a waiting strategy or a business process based on a failure type, and executing again; and feeding back the successfully executed script information to the knowledge base, and updating the content and priority of the knowledge base. Through organic combination of knowledge base enhancement, multi-candidate positioning and a self-healing mechanism, the technical problems that an existing automatic test script is high in maintenance cost, poor in stability and lack of domain knowledge are solved.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention discloses a business process multi-dimensional logic verification method and system based on a dynamic knowledge graph, and relates to the technical field of artificial intelligence and financial risk control. The method aims at solving the technical problems that a traditional rule engine cannot process unstructured data, and a large generative model has logic illusion and privacy risks. The method comprises the following steps: constructing a rule knowledge graph containing parameterized logic constraints (such as time sequence, mutual exclusion and numerical accumulation); converting the unstructured business operation record into a feature vector by using a locally deployed semantic model; performing global sequence alignment optimization by constructing a global semantic consistency objective function, mapping operation records to a theoretical decision path derived from a graph, and eliminating local matching ambiguity; and finally, on the basis of a mapping result, calling deterministic parameters in map edge attributes to execute strict mathematical logic verification (including complex workday calculation, mutual exclusiveness check and the like). According to the method, a'nerve-symbol 'dual architecture is adopted, accurate understanding of fuzzy semantics is achieved, logic verification certainty and data privacy security are guaranteed, and the method has a hidden process discovery capability.

The invention discloses a finance and taxation field agent workflow arrangement method and system based on a natural language, and the method comprises the steps: S1, widely collecting workflow arrangement cases of a finance and taxation field typical business scene, carrying out the detailed analysis of each case, and extracting a key business process and node information, S2, taking all structured workflow data as nodes, and carrying out the calculation of the workflow arrangement cases, constructing a finance and tax workflow knowledge graph by taking the semantic similarity, the function multiplexing relationship and the business process dependency relationship among all the structured workflow data as edges; s3, based on structured data and natural language description corpora constructed by the finance and tax workflow knowledge graph, performing training optimization on the base large language model by adopting a domain self-adaption and task specific fine tuning technology; s4, a user inputs a natural language demand through the interactive interface, and the natural language demand is processed by using the demand analysis agent, the retrieval agent and the arrangement agent; and S5, displaying the generated draft workflow in a visual flow chart form.

The invention provides a complex task multi-agent collaborative disassembling method and system based on a business process, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a graph through analyzing a business process document, matching a complex task with a process node, identifying a closed loop dependence path, and selecting an agent based on a constraint logic formula. And establishing a causal tracing chain to process abnormal conditions, and performing semantic alignment to ensure data transmission between subtasks. According to the method, the automation degree and the execution efficiency of multi-agent cooperative processing of complex tasks are improved, and the fault-tolerant capability of the system and the reliability of task completion are enhanced.

The invention belongs to the technical field of intelligent interaction, particularly relates to an intelligent interaction method, system and device based on AI communication and a storage medium, and aims to solve the problems that in the prior art, AI scene coverage is narrow and is separated from a business process; the system comprises four modules: a context sensing and modeling module which collects multi-source context information in real time, and generates a three-dimensional context tensor through structured alignment and time sequence normalization; the intention analysis and reasoning module performs intention level decomposition by using a deep semantic model based on the tensor, and outputs an intention recognition result and probability distribution; the response generation and adaptation module is used for generating multi-mode responses such as texts, voices, images or equipment instructions in combination with intention and context tensors, and outputting the multi-mode responses after consistency verification; and the interactive feedback and optimization module collects user explicit and implicit feedback and is used for updating model parameters and strategy weights online to realize continuous optimization of the system.

The invention relates to the technical field of business data, and discloses a process parameter iterative optimization method and system based on multi-source data, and the method comprises the steps: collecting multi-source business data, a resource association table and a task time sequence set of a target business process, analyzing the steady-state and abnormal characteristics in the data, and obtaining a target business flow; therefore, a reasonable adjustment interval of resource configuration and a to-be-improved task are determined, and a preliminary optimization scheme is formed. And constructing a resource collaboration graph by taking resources as a main body and fusing a task time sequence and a dependency relationship. After the preliminary scheme is mapped to the map, the full-node load change can be simulated and deduced. And comparing the load added value of the node in the full-node load change set with the available resource capacity of the node, and identifying a resource overload risk set of the business process. Adjusting the preliminary optimization scheme set according to the resource overload risk set to obtain a collaborative optimization execution sequence of the target business process; according to the invention, the optimization accuracy of the technological parameters of the target business process can be improved.

The invention discloses an asynchronous processing method and device for business process data, and belongs to a distributed system data processing technology, and the method comprises the following steps: S101, receiving a business request message, and persistently storing the business request message in a main message queue; s102, acquiring a to-be-processed message from the main message queue, and locking an associated target data record before executing processing; s103, verifying data consistency based on the version number recorded by the target data, if the version number is matched, executing processing and updating the version number, and otherwise, discarding the message; s104, after successful processing, sending a confirmation instruction deletion message to the main message queue; through persistent storage of the main message queue, a version number verification mechanism and management of the dead message queue, the problem of data inconsistency caused by concurrent operation is effectively prevented, reliable transmission of messages is ensured, loss of service data is avoided, dead message processing efficiency is optimized, and the method has the advantages of ensuring data consistency, preventing message loss and improving system reliability.

The invention discloses a multi-source business process self-adaptive operation and maintenance scheduling method, which relates to the technical field of business process operation and maintenance scheduling, and comprises the following steps of: under the condition that a source system state coverage behavior exists, extracting a jump rule, a behavior interruption fragment and a manual intervention identifier from a behavior acquisition vector, generating a coverage deviation feature sequence, real task execution track information under the condition that the source system state coverage behavior exists is restored based on the sequence; and forming a scheduling credibility judgment factor group by the state change mode, the behavior continuity and the intervention position in the real execution track information of the task, and forming a parameter set which can be used for quantifying the track credibility. According to the method, the problem of misscheduling caused by source system state coverage in the multi-source business process is solved, and the real restoration of the task trajectory and the accurate evaluation of the scheduling credibility are realized, so that the accuracy of the scheduling decision and the stability of system operation and maintenance are improved.

The invention relates to an inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, and discloses an inventory data synchronization and consistency verification system which realizes continuous recording of inventory operation during a network disconnection period through network state detection and local cache, performs batch transmission after network recovery through a synchronous uploading and verification mechanism, and ensures data integrity. According to the inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, data conflicts are automatically solved through a conflict detection and strategy processing module, a difference report is generated through consistency verification, and secondary synchronization is executed, so that safe synchronization and final consistency guarantee of inventory data are achieved, and it is ensured that the service process is continuous and stable. The system is characterized by comprising a network state detection module, a local cache module, a transaction log management module, a synchronous uploading module, a conflict detection module, a conflict resolution strategy module, a consistency verification module and an abnormity alarm and manual intervention interface module.

The embodiment of the invention provides an intelligent monitoring method and system based on multi-source heterogeneous data fusion. The intelligent monitoring method based on multi-source heterogeneous data fusion comprises the steps that production data sets in different production scenes are collected and stored in a classified mode; mining association features of equipment operation behavior records and material circulation track information through semantic association analysis, and extracting influence features of production environment perception data in combination with environmental factor mapping to obtain multi-source qualitative features; generating a node relation chain according to the business process association relation, and configuring a matching algorithm of qualitative scene features; screening feature subsets conforming to association rules, and combining and associating the feature subsets to obtain a scene feature combination; creating a scene exception reasoning model by using the scene feature combination and performing exception feature evolution analysis to obtain an exception reasoning result; and the abnormal propagation path is traced, the root cause node is positioned, and the abnormal monitoring label is generated, so that the accuracy of abnormal root cause positioning and the scene adaptability are improved.

The invention relates to the technical field of office automation and business process intelligent supervision driven by big data, and discloses an office operation supervision method and system based on big data. The method comprises the following steps: firstly, extracting original logs containing an initiator, a receiver and timestamps from an approval system, checking one by one, de-duplicating and sorting according to time; counting flow times based on the ordered logs, and constructing an approval flow times matrix and a directed network; calculating the initiating and receiving quantity of each node, and screening actual participating nodes; calculating a flow imbalance index for each node, adaptively setting a threshold value according to a whole network average value and a standard deviation, and identifying abnormal nodes as bottleneck candidates; an adjacent matrix is constructed for the candidate nodes, connected clustering is carried out, and a structural bottleneck group is revealed; re-calculating the imbalance index according to an isochronous window in the whole monitoring period and counting the trend; and finally, combining the static unbalance degree and the dynamic change to generate a comprehensive score, and automatically outputting a structured monitoring report.

The invention discloses a thinking chain-based power distribution and utilization fault research, judgment and analysis method and system. The method comprises the following steps: acquiring a user demand, analyzing the user demand, and matching in a preset power distribution and utilization business process template library; designing a specific task execution path and a subtask sequence, and calling a pre-constructed model library and a knowledge library to execute corresponding tasks so as to obtain an execution result; wherein multi-modal knowledge in the field of power distribution and utilization operation analysis is stored in the knowledge base, and index construction and retrieval of the multi-modal knowledge are realized; models for realizing fault research and judgment and fault analysis based on a plurality of business scenes in the field of power distribution and utilization operation analysis are stored in the model library; and integrating and forming explainable and traceable power distribution and utilization operation analysis output results based on execution results. According to the method, an advanced artificial intelligence method is introduced into the service of the power distribution and utilization field, and meanwhile power distribution and utilization fault research, judgment and analysis which are easy to use and high in interpretation are achieved.

The invention provides an intelligent agent training management method, system and device, and belongs to the technical field of intelligent agents, and the method specifically comprises the steps: taking an interaction demand type between a virtual user and an intelligent agent as a basis, combining the change conditions of interaction user data in different time periods, and carrying out the training management of the intelligent agent; determining an available interaction time period between the virtual object and the intelligent agent, and determining an analysis processing strategy of interaction data between the interaction user and the intelligent agent according to the data of the available interaction time period; according to the method, the interaction management strategy of the virtual object in the time period except the available interaction time period is determined according to the analysis processing result of the interaction data of the virtual object in the available interaction time period and the interaction data between the interaction user and the intelligent agent, so that the updating processing efficiency of the intelligent agent is improved, and meanwhile, the influence on a normal business process is also reduced.

The invention discloses a natural resource big data off-line comprehensive analysis, annotation and proof system and method, and belongs to the technical field of natural resource informatization and geographic information processing. The system comprises the following components: a spatial data fusion module, which is used for realizing unified management and efficient processing of heterogeneous spatial data; the spatial index optimization module is used for realizing quick retrieval and high-performance query optimization of spatial data; the intelligent annotation and arrangement module is used for realizing low-code rapid configuration and business process automatic arrangement of different natural resource investigation scenes; the off-line analysis module is used for realizing buffer area analysis, space superposition and terrain analysis in an off-line environment; the real-time visualization module is used for realizing multi-level map display, track recording, vector element editing and multimedia evidence collection; and the data quality management module is used for realizing automatic quality inspection of geometry, topology and attributes and statistical analysis, version control and export management of annotation results.

The invention relates to the technical field of software engineering and information processing, in particular to a semantic verification method and device based on a large model.The method comprises the steps that a source code file and a corresponding software requirement and design document are obtained, code semantic representation is generated based on grammar analysis and static program analysis, and a semantic verification result is obtained; based on text analysis and business flow chart structure identification, generating a design demand semantic representation; respectively inputting the code semantic representation and the design demand semantic representation into a pre-training language large model to generate a code implementation logic description and a design demand logic description; and performing semantic consistency verification on the two types of logic descriptions to obtain a semantic inconsistency result, determining a code position in the source code file based on the semantic inconsistency result, and outputting a scanning report. According to the invention, the automation degree and the rechecking performance of consistency checking of requirements can be improved, and the leak detection and reworking cost can be reduced.

The invention provides a visual configuration analysis system for a power supply service event, and the system achieves the unified access and deep fusion of multi-source heterogeneous data through a data fusion module, and generates high-quality power grid situation awareness data. Abstracting physical resources into standardized service units through a resource packaging module, performing centralized management, and constructing a real-time resource pool; through the visual low-code arrangement module, business personnel can flexibly define and modify complex business processes and scheduling requirements in a graphical mode, and the technical threshold is greatly reduced; the metadata processing engine automatically explains and executes the arranged service logic, and intelligently matches and schedules optimal resources according to requirements; and finally, the operation state and result of the system are visually presented through the visual rendering module. The intelligent level and response efficiency of power supply service event processing are improved, flexible configuration and automatic execution of the process from data to resources to business are achieved, and the agility of a power grid to cope with complex events and the reliability of power supply service are enhanced.

The invention relates to the technical field of airport resource management, in particular to an airport ground support resource dynamic optimization method and system based on digital twinning, and the method comprises the steps: analyzing an airport business process knowledge graph, and fusing multi-modal data to construct a generative digital twinning model; performing multi-scene simulation deduction and multi-target optimization based on the generative digital twin model to generate a long-term planning scheme; during the execution period of the long-term planning scheme, monitoring the deviation between the actual operation data and the scheme predicted value, and performing dynamic adaptive adjustment on the long-term planning scheme; and combining a long-term planning scheme with real-time operation data to provide decision support, evaluating an optimization effect, and taking an evaluation result as feedback to iteratively optimize the generative digital twinborn model. According to the method, the generation type digital twinborn model is constructed, and a double-layer closed-loop optimization mechanism of long-term planning and short-term correction is established, so that the overall efficiency and punctuality rate of airport ground guarantee and the toughness of coping with emergencies can be effectively improved.

The invention discloses an enterprise-level agent system and a data processing method. The system mainly comprises a sensing module, a memory module, a planning module, a Routine agent planning engine, an arrangement engine and an execution module. According to the enterprise-level agent system provided by the embodiment of the invention, short-term memory, long-term memory and enterprise-level memory are combined through a complete closed-loop architecture of the sensing module, the memory module, the planning module and the execution module, and precipitation of personalized and organization-level knowledge is supported; and meanwhile, in combination with a standard business process template provided for an enterprise by a Routine agent planning engine and splitting of a task process by an orchestration engine, role agents are allocated for each sub-task, so that coordination and cross-system orchestration of complex tasks are realized, the construction threshold of intelligent applications is reduced, and the business efficiency is improved.

The invention discloses a business processing method and device based on an ant colony algorithm, electronic equipment and a medium. The method comprises the following steps: acquiring a business service request, and analyzing the business service request to obtain a business task; determining each agent matched with the business task; wherein the intelligent agents are used for cooperative processing of business tasks; planning the cooperation path of each agent based on an ant colony algorithm to obtain a target path; and according to the cooperation sequence of the intelligent agents in the target path, executing the business task by using the intelligent agents in sequence. According to the technical scheme, efficient collaboration between agents is achieved through the improved ant colony algorithm. According to the system, a multi-dimensional pheromone matrix is adopted to record and transmit collaborative experience among departments, a knowledge verification mechanism based on swarm intelligence is established, and dynamic optimization and rapid convergence of a cross-department business process are realized.

The invention relates to the technical field of insurance business management, and discloses a multi-level-oriented insurance business index dynamic monitoring and intelligent decision-making system, which comprises a data acquisition module used for acquiring multi-level business data involved in an insurance business process in a multi-source manner, and the multiple levels comprise at least two levels with different management dimensions; and the data processing and standardization module is connected with the data acquisition module and is used for cleaning, converting and standardizing the acquired multi-level business data to generate structured data in a unified format. By constructing a multi-level index system and realizing dynamic monitoring of cross-dimensional business data, and combining intelligent early warning and root cause tracing functions, changes of operating indexes of each level can be linked in real time, so that insurance enterprises can accurately master operating states from high-level management to basic-level business links, quickly identify abnormities and position roots, and the safety of the insurance enterprises is improved. And the limitation of data splitting of each level and each dimension in traditional index monitoring is effectively broken through.

The invention provides an RPA operation report automatic generation method based on full-business process data fusion and a financial robot thereof, and the method comprises the steps: collecting task logs, business action events and context data generated in the execution process of the financial robot, and standardizing the task logs, the business action events and the context data into a unified event unit; constructing a flow path diagram based on the event time sequence; performing structure comparison on the trajectory diagrams of different service periods, identifying node addition and deletion or path rearrangement, and generating a structure attribution result; combining the attribution result and the event data to construct a structure perception type semantic index; and finally, automatically filling the indexes and attribution contents into the financial operation report template to generate a complete report. The method does not need to depend on a process definition file, can automatically restore a real execution path, achieves the interpretability analysis of process change and the full-automatic generation of a report, and remarkably improves the accuracy, integrity and decision support capability of financial RPA operation analysis.

The invention relates to the technical field of software engineering and automatic testing, in particular to an E2E scene test optimization method and system based on target man-machine cooperation. According to the scheme, an optimization target is manually set, an initial test case and a business process are input, and a test resource library is constructed; the system automatically analyzes a service path, generates a use case dependency graph and a key path, and determines a core scene through manual verification; a candidate optimization strategy is generated by the system, and a target strategy is determined through manual screening according to resources and priorities; the system carries out simplification, merging and scheduling optimization on the use cases, and deploys and executes the use cases after manual sampling verification of coverage; the system collects execution data in real time and generates an evaluation report, and whether a use case is supplemented or a strategy is adjusted is determined after manual analysis; and finally updating the resource library and the strategy model, establishing a dynamic feedback mechanism, and triggering continuous optimization during system iteration. According to the method, the E2E test efficiency and the software delivery quality are remarkably improved through man-machine cooperation and closed-loop optimization.

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 relates to the technical field of insurance information, and discloses a personal insurance fraud full-process prevention monitoring system and method, and the system comprises a data collection and fusion module which is used for extracting data in real time or quasi-real time from business systems of insurance buying, underwriting, preservation, claim settlement and the like in an insurance company, external data such as medical records, credit investigation data, judicial information, internet financial behavior data, black and grey lists shared by other insurance companies and the like are integrated through an API interface, and multi-source heterogeneous data are cleaned, standardized and associated; and the multi-modal risk analysis engine comprises a rule engine layer, a machine learning model layer and a correlation graph analysis layer. A prevention and monitoring system covering a personal insurance full-service process is constructed, and anti-fraud monitoring is preposed to insurance and underwriting links and runs through a claim settlement link to form a closed loop. A traditional afterward response mode is changed, and early warning can be given out and intervention measures can be taken at the early stage of risk occurrence.

The invention provides a message sorting method and device, electronic equipment and a program product, original messages are obtained through a distribution thread pool, the messages of the same business process are distributed to the same to-be-sorted queue, a working thread of the sorting thread pool reads the messages and stores the messages in a thread local cache set, the sendability is judged based on the dependency relationship of the messages in the business process, and the message sorting efficiency is improved. The messages meeting the conditions are sent to the subscription queue, and the messages not meeting the conditions are reserved in the cache. According to the mechanism, the problem of disorder of messages of the same service caused by multiple switches and network delay of message middleware is effectively solved, and data inconsistency and service abnormity are avoided; unified sorting logic replaces repeated development of subscribers, so that the system complexity is reduced; through thread pool division, local cache storage and a cache strategy of messages which do not meet conditions, the interaction overhead is reduced, the message orderliness and low delay requirements are considered in a high-frequency transaction scene, and the processing throughput, the real-time performance and the resource utilization rate under high concurrency are improved.

Disclosed is a business process infrastructure container system. The system includes one or more business process infrastructure modules configured to perform a user-desired operation on a client platform. The system also includes a manifest file having details of one or more business process infrastructure modules. The system further includes computer-readable instructions configured to trigger the user-desired operation via one or more business process infrastructure modules and the manifest file.

The invention provides a test case sequence generation method and device, equipment and a storage medium. The method comprises the following steps: acquiring calling data of an interface in a business process in real time by adopting a probe; for each interface, multi-dimensional data features are extracted according to the corresponding calling data, and the data features comprise data fingerprints, semantic vectors and structural hash; performing feature matching according to the data feature of each interface to determine a dependency relationship between the interfaces; constructing an interface business flow chart according to the dependency relationship between the interfaces; and generating a test case sequence according to the interface business flow chart, associating the test case sequence with the static data template, and executing a test through dynamic injection parameters. According to the method, the interface dependency relationship can be accurately and automatically identified by extracting multi-dimensional features such as data fingerprints, semantic vectors and structural hash from the calling data and matching the multi-dimensional features, the interface business flow chart is constructed based on the dependency relationship, the test case sequence is generated according to the interface business flow chart, and the test efficiency and reliability are improved.

The invention is suitable for the technical field of vehicle diagnosis, and provides a vehicle diagnosis system and method, and the system comprises a processor which is connected with a plurality of electronic control units (ECUs) of a vehicle and a vehicle diagnosis device, and is used for analyzing a diagnosis configuration file in a preset folder under the condition that the vehicle diagnosis device establishes communication connection with the plurality of ECUs, and sending the diagnosis configuration file to the vehicle diagnosis device; and obtaining diagnosis configuration data of the plurality of ECUs. According to diagnosis business process parameters in the diagnosis configuration data, creating a corresponding software channel for each ECU, and packaging all configuration parameters of each software channel in corresponding straight-through structure examples; and managing straight-through structure instances of all software channels through a pointer array and a thread channel mapping table in the global management structure body. And the server is also used for receiving the global channel configuration parameters through the universal thread pool and matching a corresponding working thread for each software channel. According to the system, synchronous diagnosis of a plurality of ECU systems is realized by establishing a parallel diagnosis architecture, and the efficiency of vehicle diagnosis is improved.