476 results about "Exception handling" patented technology

The invention provides a large model agent collaborative scheduling method and system oriented to complex tasks, relates to the technical field of artificial intelligence, and comprises the steps of task decomposition, feature extraction, agent matching, dynamic scoring, scheduling scheme generation and optimization, execution monitoring, exception handling and the like to realize efficient collaboration of large model agents. According to the method, accurate matching can be carried out according to task characteristics and intelligent agent capabilities, the task completion efficiency and quality are improved, meanwhile, the dynamic adjustment capability is achieved, abnormal conditions in the execution process are effectively handled, and the system robustness is enhanced.

The invention relates to the technical field of equipment abnormity control, and discloses an equipment abnormity self-closed-loop control system. An anomaly detection engine of the system collects operation state data of target equipment in real time, and performs preliminary anomaly marking according to a preset anomaly judgment rule; the dynamic analysis unit receives the preliminary anomaly marks, generates anomaly feature vectors in combination with historical operation data, and inputs the anomaly feature vectors into a multi-dimensional evaluation model to divide anomaly levels; a strategy generator is matched with a preset repair strategy library according to the abnormal grade division result, and a corresponding repair instruction sequence is generated; the execution control module analyzes the repair instruction sequence, and dispatches hardware resources to execute repair operation according to a preset execution priority; and the feedback verification unit collects the running state data again after restoration, and compares the running state data with the initial abnormal mark to generate a restoration verification result. The system realizes full-process automatic processing of equipment exceptions, and improves exception processing efficiency and equipment operation stability.

The invention discloses a dynamic script compiling and hot updating system based on Grouping and an implementation method of the dynamic script compiling and hot updating system. The system comprises a script source code management module, a dynamic compiling engine, an intelligent cache manager, an exception handling and recovery system and a hot update controller. And the script source code management module receives and preprocesses the Grouping script to generate a unique identifier for subsequent management. And the dynamic compilation engine executes compilation operation according to the identifier to ensure that a compilation result accords with a specification. And the intelligent cache manager efficiently stores and retrieves the compiling result, so that the repeated compiling cost is reduced. And the exception handling and recovery system monitors the execution state in real time, and automatically starts a recovery process in case of exception to guarantee stable operation of the system. The hot update controller realizes seamless update and version switching of scripts, and ensures service continuity. Through cooperative work of the modules, high-reliability, high-performance and zero-shutdown deployment is realized.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention discloses a scheduling instruction real-time monitoring system for streaming data processing, which relates to the technical field of streaming data processing and comprises a data access module, an instruction analysis module, a dynamic scheduling strategy module, a real-time monitoring module, a scheduling execution module, a visual monitoring interface, an exception handling module and an extension module. The data access module is used for receiving a multi-source heterogeneous data stream in real time and carrying out protocol analysis and priority marking on input data, and the instruction analysis module is connected with the data access module and is used for extracting a scheduling instruction in the data stream and converting the scheduling instruction into executable operation; and the dynamic scheduling strategy module generates a dynamic scheduling strategy based on the real-time data traffic, the system load and a preset rule. Through the dynamic scheduling strategy module and real-time monitoring feedback, the system can automatically adjust resource allocation according to real-time load prediction, and dynamic balance of high resource utilization rate and low delay is achieved.

The invention discloses an intelligent timed task configuration and multi-mode feedback system based on natural language interaction, and relates to the field of industrial automation control, enterprise-level task scheduling management and instant messaging. The system comprises a natural language interaction module, a user natural language instruction is converted into structured semantic information through a BERT + BiLSTM + CRF mixed architecture, and lightweight processing is achieved in combination with TinyBERT distillation; the dynamic task arrangement module completes task creation, modification and the like based on structured information, completes missing parameters through historical task mode matching, and supports multi-session real-time synchronization; the rich media report generation module generates a multi-mode report containing charts, tables and the like, and the RAG technology and large model interpretation are combined; and the multi-channel pushing adaptation module adapts multiple platforms by adopting a strategy and factory mode. According to the system, the task configuration efficiency and flexibility are improved, the feedback form is enriched, the information pushing accuracy is guaranteed, and the exception handling capacity is enhanced.

The embodiment of the invention provides an application log exception processing method and device, equipment and a storage medium, and the method comprises the steps: extracting exception information corresponding to an application identifier from original log data, and determining a target keyword and a target time range based on a preset alarm rule and the exception information; determining candidate log data from the original log data according to the target time range and the target keyword, and filtering the candidate log data to obtain target log data; inputting the target log data and a set prompt text into the target network model to obtain a problem description text and a solution description text; and determining a manual processing description text associated with the problem description text from a stored manual processing record corresponding to the application identifier, fusing the solution description text and the manual processing description text to obtain a target scheme description text, and feeding back the target scheme description text to the background server. According to the scheme, the log exception processing efficiency can be improved.

The invention provides a control method, device and equipment for an FMS production line warehouse system based on fault warning and elimination verification and a storage medium. The control method comprises the steps that material attribute information is acquired, a classification model based on machine learning is adopted to conduct classification and recognition on materials, a unique material identification code is generated and bound with an electronic tag, and a real-time material tracking system is established; a storage position distribution model based on multi-objective optimization is constructed, material relevance, use frequency and warehouse constraint conditions are considered, a material storage position distribution scheme is generated, and a corresponding carrying equipment control instruction is generated; a real-time monitoring and exception handling mechanism is established, and when it is detected that the actual state of the material does not accord with the system record, a fault warning process is triggered, an exception handling control instruction is generated, and a correction action is executed; the material demand prediction is carried out based on the neural network model, the replenishment control instruction is generated according to the prediction result, the accuracy of the execution result is ensured through a secondary verification mechanism, and the reliability of the warehouse system is improved.

The invention discloses an agent assisted testing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring demand information of a Web system to be tested; analyzing the demand information and generating a test case set; performing hierarchical labeling on the test case set to form a hierarchical case set containing the basic operation type case, the data verification type case and the complex logic type case; distributing the basic operation type use case to an execution module of the agent system, distributing the data verification type use case to an interface automation test module, and distributing the complex logic type use case to an artificial interaction terminal; and when the execution module or the interface automatic test module monitors that the case execution fails or the state is abnormal, an exception processing strategy is matched according to an exception type and an exception source, corresponding processing operation is executed, and feedback information used for optimizing the layered case set is generated. According to the method, the test tasks can be intelligently allocated and accurately executed, and automatic adaptation and optimization can be performed when an exception occurs, so that an efficient man-machine collaborative test is formed.

The invention relates to an automatic interface test management method and device, equipment and a medium, and the method comprises the steps: obtaining an interface document of a to-be-tested interface, carrying out the analysis of the interface document, generating interface case data containing the primary configuration content of an interface case, and transmitting the interface case data to a server; performing execution grouping on the interface cases configured with the input parameter value set, generating a case execution group based on interface grouping information and a preset test execution strategy, setting an execution sequence and an exception handling rule for the case execution group, and generating a corresponding execution link at the same time; calling an execution link based on the trigger request to sequentially use each group of request content execution cases in the input parameter value set to execute the interface cases in the group, and generating corresponding case execution results; high automation and intelligentization are achieved in the whole interface test process, manual intervention is remarkably reduced, the test coverage rate is increased, the problem response speed is increased, and therefore the working efficiency of testers is effectively improved, and the human error risk is reduced.

The invention discloses a power embedded RTOS-oriented clock management method and related equipment, and the method comprises the steps: collecting the current clock data of a power embedded RTOS system, carrying out the feature extraction of the current clock data, constructing a clock feature library, and constructing a clock data model in a normal clock operation state, generating normal clock data according to the clock data model; the frequency stability, the clock jitter rate and the clock drift rate of the current clock data are calculated, and whether the deviation between the current clock data and the normal clock data exceeds a clock threshold value or not is judged; and when the deviation exceeds the clock threshold value, executing a corresponding exception handling flow according to the frequency stability, the clock jitter rate and the clock drift rate, and dynamically adjusting the range of the clock threshold value. According to the embodiment of the invention, combined judgment and dynamic adjustment can be carried out by combining drift, jitter and frequency stability thresholds, and accurate monitoring and anomaly detection of the clock are realized. The method can be widely applied to the technical field of clock management.

The invention relates to the technical field of internet-of-things control management, in particular to an intelligent internet-of-things control management system based on commercial operation, which comprises a data acquisition and alignment module, a strategy generation and authority module and an instruction execution and exception handling module. The data acquisition and alignment module is used for acquiring an equipment state, an environment parameter and a user instruction, and completing standardized processing and space-time alignment to generate a unified data set; the strategy generation and permission module generates a control strategy according to a service rule, divides permissions according to user roles, and generates a role instruction set; and the instruction execution and exception handling module issues an instruction to the equipment, monitors an execution state, and triggers rollback, work order generation and permission verification mechanisms when an exception occurs. According to the invention, high-precision sensing, intelligent control and safe and efficient Internet of Things equipment management are realized.

The invention relates to the technical field of Internet of Things, in particular to a decision-making method for computing power balanced allocation between Internet of Things devices, which comprises a task allocation module, a resource management module, a dynamic adjustment module and a priority regulation and control module. The task distribution module decomposes tasks and generates a preliminary distribution scheme; the resource management module monitors equipment performance and classifies the equipment performance; the dynamic adjustment module optimizes an allocation strategy by using a particle swarm optimization algorithm; and the priority regulation and control module regulates the task sequence according to the weight and ensures the high-priority task resource demand. The system stability is enhanced through the exception handling unit and the load prediction unit, the computing power distribution efficiency and the resource utilization rate are improved, and efficient and flexible task execution is achieved.

The invention discloses a welding wire production control system and method based on visual feedback, and belongs to the technical field of precision machining. The system comprises a multi-mode intelligent sensing subsystem, a self-adaptive model prediction controller and a safety and exception handling subsystem, and a two-stage control architecture of a quality closed loop and an execution closed loop is constructed. According to the method, the microcosmic roughness, macroscopic geometry, defects and other multi-dimensional quality characteristics of the surface of the welding wire are obtained in real time through multi-modal sensing and fused into a comprehensive quality index; carrying out rolling optimization by adopting a model prediction control algorithm for updating parameters on line, and generating a control instruction; meanwhile, trend early warning based on statistical process control and sudden fault diagnosis based on model residual analysis are operated in parallel, and active safety protection is achieved. According to the method, the problems of unstable quality, control lag and passive safety caused by supplied material fluctuation and tool time variation in the welding wire flaking machining process are solved, and self-adaptive accurate control and intelligent safety protection in the machining process are achieved.

The invention discloses an intelligent dynamic VHD merging technology based on AI prediction, and belongs to the technical field of virtualization. According to the technology, virtual machine indexes are monitored through an AI prediction module, the merging opportunity is predicted, and VHD merging of the virtual machine in the online state is achieved in combination with a merging control module, an online merging module and an exception handling module. The method specifically comprises the steps of training a decision tree model based on historical data, and predicting a future disk growth trend; creating a temporary snapshot and redirecting a write operation, wherein a background merging thread only processes an effective modification block; magnetic disk switching is completed through atomic operation, and data consistency is ensured; and a rapid rollback mechanism is provided, and the service continuity is ensured. According to the method, the problems of fixed threshold, off-line merging, full-amount merging and the like in the prior art are solved, intelligent prediction, on-line merging and increment optimization are realized, and the efficiency and reliability of VHD merging are improved.

The invention discloses a development board Linux system security authentication multi-start method, which comprises the steps of 1, hardware trust root initialization and storage area division, 2, hardware trust root self-inspection and measurement, 3, starting item dynamic loading and multi-stage authentication, 4, security context establishment and system switching, and 5, multi-system isolation and collaboration. Step 6, performing security audit and exception handling; full-link authentication is realized by taking a hardware trust root as an anchor point, malicious mirror startup and inter-system data leakage are effectively resisted in combination with a storage and resource isolation mechanism, scenes such as on-demand dynamic switching, flexible adaptation debugging, upgrading and fault recovery of multiple systems can be realized, and the system reliability is improved by optimizing the lightweight design of an authentication algorithm and resource management. Limited computing power of the development board is adapted, the starting process and abnormal events can be completely recorded, and illegal operation can be traced conveniently.

The invention provides a state machine management method based on a hierarchical architecture and a dynamic trigger mechanism, which adopts a four-layer nested state hierarchical architecture and comprises the following steps: acquiring an equipment initial state signal and starting a state machine working process; switching from an initial state to a task state according to a trigger mechanism, and executing a task action; determining a state transition path according to a task state execution result, switching to a stable state and executing a resource synchronization operation if task execution succeeds, and switching to a fault state and executing an exception handling action if task execution fails; and determining whether to recover to a task state or enter a safety mode according to a fault state processing result. According to the technical scheme, the reliability and the operation efficiency of the electric energy meter equipment are effectively improved, and the fault recovery capability and the resource management capability of the system are enhanced.

The invention provides an operating system installation method and device, equipment and a storage medium, and can be applied to the technical field of operating systems. The operating system installation method comprises the following steps: in response to an exception occurring in a process of installing a to-be-installed operating system, obtaining an error type of the installation exception based on a preset exception handling module in an installation program; according to the error type, determining a processing mode for the installation exception; in response to the processing completion instruction, target configuration information is obtained, the processing completion instruction indicates that processing of the installation exception is completed based on the processing mode for the installation exception, and the target configuration information is configuration information obtained after original configuration information is modified according to the error type of the installation exception of the to-be-installed operating system; and according to the target configuration information, reloading the installation program so as to install the to-be-installed operating system.

The invention relates to the technical field of meteorological and ocean numerical forecasting, and aims to solve the problems of strong manual dependence, poor timeliness, high error risk and low resource utilization rate in coupling operation of a WRF atmosphere model and an ROMS ocean model. The method comprises the following steps of: storing an initial file into a date catalogue which is dynamically generated according to a YYYMMDD format after the initial file is subjected to integrity verification; the timed task checks nc.lock / nc.tone files every X minutes, a lock starting model is created when no lock exists and an initial file exists, and an ROMS needs to confirm nc.tone of a WRF result directory first; automatic exception processing is carried out when the model is overtime, and after completion of the automatic exception processing, a Done is created, a lock is deleted, resources are released, and computational nodes are dynamically allocated. According to the method, automatic scheduling is achieved through double-file marking, manual operation is shortened to be within one minute, model connection delay is shortened to be within five minutes, the failure rate is reduced, the resource utilization rate is improved, and a reliable scheme is provided for model business operation.

The invention relates to a dynamic configuration management method and system under a stand-alone lightweight scene, and the method comprises the steps: carrying out the unified description of a configuration file of an application program through the form of structured metadata, and obtaining a metadata configuration item, so as to achieve the decoupling of the configuration file and a business code; in the starting process of the application program, the metadata configuration items are detected and verified, and the metadata configuration items meeting preset requirements are cached in a key value hash table of a volatile memory; and after the application program is started, acquiring a configuration value corresponding to the metadata configuration item from the key value hash table of the volatile memory through the configuration item acquisition interface. Through the application, the configuration item is defined in the form of metadata to realize decoupling with the service code, the verification is configured in the application starting stage to realize timely abnormity, the operation and maintenance cost is effectively reduced, the hash table cache effectively reduces the access frequency of the configuration file in the disk, and the problem of how to improve the management efficiency of the configuration item of the application program is solved.

The invention discloses an abnormality diagnosis and repair method and device for an automatic process of a robot and a medium, and relates to the technical field of artificial intelligence. The anomaly diagnosis and repair method for the automatic process of the robot comprises the following steps: acquiring RPA anomaly processing data; matching the RPA exception processing data with a target causal knowledge base to obtain an exception scene repair action; and according to the abnormal scene repair action and the large language model, generating a process repair script, and according to the process repair script, performing abnormal scene process repair. According to the technical scheme provided by the embodiment of the invention, the handling effect of the PRA in the exception handling scene can be greatly improved on the premise of low maintenance cost.

The invention relates to the technical field of industrial automation, in particular to a production line exception handling method and device, computer equipment and a medium. The method comprises the following steps: in response to a production abnormal event of a production line, acquiring multi-dimensional state sensing data corresponding to the production abnormal event; according to the multi-dimensional state sensing data, constructing an exception handling decision tree corresponding to the production exception event; based on the exception processing decision tree, determining an optimal processing path for solving the production exception event; according to the method, the limitation of a traditional static mapping mechanism in coping with'same symptom and different cause 'and'multi-exception concurrence' scenes can be broken through, self-adaptive response and accurate disposal of complex exception scenes are realized, and the method is high in practicability and high in practicability. The intelligent level and response efficiency of production line exception handling are remarkably improved, and reliable guarantee is provided for stable operation of a production line.

Example solutions for providing an artificial intelligence (AI) exception interpreter are disclosed. An exception interpreter receives an exception report from a software application, evaluates it (using AI), and generates a first exception handling response. This provides the benefit of learning over a large amount of exception handling data, which offers super performance over trial-and-error tactics by programmers who are either less experienced or unfamiliar with exceptions from third party resources. Some examples sanitize exception reports of sensitive information. Some examples track an exception event history, so that when the same exception is seen multiple times (meaning a prior response was not effective), a different exception handling response may be provided.

The invention discloses a condition analysis execution method and device based on dynamic configuration and computer equipment. The method comprises the following steps: acquiring a condition expression and corresponding data; constructing a logic tree structure; traversing condition items; judging whether the condition item is a leaf node or not; if yes, inquiring an operator registration library to determine whether an operator exists or not; if yes, executing an operation method, and performing type self-adaptive processing to obtain a Boolean result; recording Boolean results; judging whether the current condition item is the last condition item or not; if yes, applying root logic to output a result; if not, performing exception handling; and if the condition item is not the leaf node, recursively processing the sub-conditions, aggregating results obtained by processing to obtain a Boolean result, and recording the Boolean result. By implementing the method provided by the invention, flexible and easy-to-maintain condition processing capability can be provided, and the problems of framework rigidity, high maintenance cost, function limitation and the like in the prior art are effectively solved.

The invention discloses a rule engine and low-code dual-drive power grid digital graph fusion treatment platform and method. The platform comprises an infrastructure layer, a data layer, an algorithm engine layer and a business application layer. And the data layer is used for accessing and managing multi-source power grid data. And the algorithm engine layer comprises a rule engine module for configuring and executing data verification, digital graph association and exception handling rules, a data quality diagnosis algorithm module for performing data quality diagnosis to identify exceptions, and a digital graph fusion driving engine module for realizing bidirectional linkage between data and graphs. And the business application layer comprises a low-code arrangement platform for customizing a governance scene page and a work order process, a digital graph integrated display module for displaying multi-source power grid data on the same screen, and a closed-loop work order management module for automatically generating and distributing governance work orders. The method constructs a depth digital graph fusion system, improves the anomaly positioning and treatment efficiency, achieves automatic management and control and whole-course tracing, does not need a large amount of code development, and shortens the response period of a new business scene.

The invention discloses a structured information extraction method and system based on a large language model, and the method comprises the following steps: carrying out the modular cue word design and adaptive loading, so as to guide the large language model to carry out the content generation according to the expected structure and logic; inputting text type identification and task routing to ensure that each type of information can be processed by adopting the most appropriate model prompt strategy; automatic segmentation and concurrent processing are carried out to ensure information integrity and processing efficiency; semantic completion and key information enhancement are carried out to carry out semantic completion on the entity information; performing structured format conversion and standard packaging to output a standardized format; and performing exception processing and full-process log recording to perform real-time tracking and backtracking on the state of each processing task. According to the method, the key information in the text can be automatically recognized, extracted and output in a structured mode, and the information processing efficiency and accuracy are improved.

The invention discloses a root cause alarm analysis method and system based on time sequence association and multi-modal fusion, and the method comprises the steps: taking digital twin dynamic threshold cleaning as a core, fusing multi-source data time sequence alignment verification, and forming a multi-source data set of time sequence alignment and preliminary restoration through exception handling and cross-system time consistency verification; a cleaning threshold is optimized in combination with a working condition to filter noise, a label confidence weighting method is fused, confidence is given to a label through twin simulation, and a multi-modal data set with a multi-dimensional label is constructed. A learning model is constructed and trained, exclusive feature extraction branches are designed for multi-source data, a twin working condition adaptation enhancement module is introduced to strengthen core modal extraction, and the proportion of abnormal modals in fusion is reduced by means of a dynamic attention filtering mechanism. Based on the model and a twinning system, root cause penetration type positioning is completed through dynamic sequencing of root cause confidence in combination with a twinning reduction time sequence. And the root cause analysis efficiency and accuracy are improved.

The invention discloses an automatic test method and test platform based on cloud computing, and belongs to the technical field of software testing, and the method comprises the steps: firstly constructing a dynamic elastic resource pool containing a virtualized test node cluster, a tool chain and a distributed data warehouse; after obtaining a performance test request, analyzing data to screen an optimal node combination and tool configuration, and generating a test case set covering a boundary scene and an abnormal path in combination with historical data and reinforcement learning; using an improved genetic algorithm and a load prediction model to disassemble the subtasks and intelligently distributing the subtasks; the progress is tracked in real time through a digital twinborn model, abnormity is recognized, and a multi-stage processing mechanism is activated; and after the test is finished, normalizing the processed data, and generating a panoramic report by means of the analysis model. According to the method, the intelligent level, the execution efficiency, the resource elasticity and the exception handling capacity of automatic testing are improved, and the high-performance testing requirement in a complex scene can be met.

The invention discloses an implementation method of a workflow integrated thinking chain system, which comprises the following steps of: defining three types of core nodes, namely function nodes, logic nodes and integrated nodes, constructing a chain structure supporting dynamic generation, and realizing seamless integration among multiple workflows; according to the method, user input is disassembled into subtask nodes through semantic analysis and a predefined template, and a thinking chain is dynamically generated and adjusted in combination with intelligent logic modeling, real-time data analysis and dynamic rule iteration; meanwhile, the task scheduling and execution module calls an interface according to chain logic to complete node operation, and supports concurrent processing and state monitoring; the exception handling and self-optimization mechanism optimizes chain logic and improves execution efficiency and reliability through real-time exception detection, fault recovery and artificial intelligence analysis based on historical data.

The invention discloses an AI-based micro-service system exception handling method and system and a storage medium, and belongs to the technical field of software exception monitoring. The method comprises the following steps: in an exception real-time capturing stage, deploying a global exception intercepting component at a micro-service gateway or each service framework layer, and generating an exception report; in the AI anomaly analysis stage, the anomaly report is analyzed, and a solution thought or a code repair suggestion is generated; an active alarm and notification stage: notifying development, operation and maintenance personnel according to an AI analysis result; meanwhile, all abnormal information is pushed to a monitoring panel; in the automatic repairing and deploying stage, exception handling is carried out, and dynamic deploying and system self-healing of exceptional patches are achieved; in the feedback learning and iterative optimization stage, exception processing results are collected, and the diagnosis accuracy and the repair suggestion quality are improved by continuously learning new exception cases and processing modes. According to the invention, abnormal information from different services is collected in a unified manner through a global interception mechanism, and the omission risk caused by decentralized processing is eliminated.