1915 results about "Troubleshooting" patented technology

The invention discloses a low-voltage transformer area examination meter practical training device and a fault simulation control method, and relates to the technical field of electric power practical training. The device aims at the problems of single scene, extensive evaluation and low skill improvement efficiency in traditional electric power practical training. The meter simulation module is compatible with various meters and is matched with a dual-communication protocol to realize efficient data transmission; the intelligent interaction module provides high-quality interaction experience through a high-definition touch screen and low-delay transmission; the data generation module can quickly switch customized scenes, and the fault simulation module can accurately inject various faults; the training management module depends on advanced technologies of knowledge maps, deep learning and fuzzy comprehensive evaluation to realize full-process closed-loop management from project design and operation guidance to evaluation feedback and intelligent optimization; all the modules cooperate efficiently, and the professionality, the accuracy and the efficiency of power failure troubleshooting and operation and maintenance skill training of trainees are remarkably improved.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

The invention discloses a risk and hidden danger investigation and evaluation method and system for chemical enterprise production management, and relates to the technical field of enterprise risk and hidden danger investigation, and the method comprises the steps: collecting multi-source monitoring data of a chemical factory in real time through an edge sensing network, uploading the multi-source monitoring data in real time, and synchronously recording data block chain information; based on the three-dimensional model and the physical and chemical mechanism model, constructing a three-dimensional twinborn model, simulating an abnormal working condition, generating a dynamic risk thermodynamic diagram, and marking a risk hidden danger area; fusing multi-source monitoring data to construct an equipment fault knowledge graph, and associating a fault phenomenon with a fault reason; and responding to the risk hidden danger troubleshooting operation and carrying out process evidence storage. According to the method, the limitation of static monitoring and isolated evaluation is broken, an intelligent full-process risk management system with data, model and knowledge collaborative updating is formed, the real-time performance, the accuracy and the automation level of risk early warning are remarkably improved, and the scientificity and the sustainability of production safety management are enhanced.

The embodiment of the invention discloses a causal chain-based front-end state anomaly detection method and system. The method comprises the following steps: S1, identifying a calling relation among an event monitor, an asynchronous task and a state change logic by analyzing a source code, and constructing an expected causal chain model; s2, dynamically verifying whether the actually occurring causal chain conforms to an expected model or not during the application running period; wherein in the step S1, the analysis of the source code comprises but is not limited to an AST technology; in the step S2, the hijacking mode of the key function comprises but is not limited to a Proxy mechanism, function packaging and a Hook technology. According to the method, the expected causal chain model is constructed, the actual path is dynamically verified during operation, system inconsistency caused by causal chain abnormity is avoided, the AI model is utilized to generate the repair strategy suggestions, a traditional low-efficiency troubleshooting process depending on logs and breakpoint debugging is upgraded to intelligent decision, and the repair efficiency is remarkably improved.

Systems and methods described herein relate to automated troubleshooting for application development tools. First data comprise a plurality of delta objects and a user query. The plurality of delta objects identify design-time changes made to an application using a development tool. The user query is received via a user interface and relates to at least one of the design-time changes. The first data is preprocessed to obtain second data. The preprocessing of the first data includes modifying a subset of the first data and adding one or more predetermined instructions to the first data. The second data is provided to a machine learning model to obtain a response to the user query. Output indicative of the response is caused to be presented in the user interface.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is achieved.

The invention provides an artificial intelligence-driven energy storage system health state monitoring and early warning mechanism. The mechanism comprises five levels of data acquisition, processing, artificial intelligence analysis, decision support and user interaction. System operation data is collected in real time through a sensor network, and after cleaning and standardization, machine learning and deep learning algorithms are used for analysis. Based on the analysis results, the system provides optimization suggestions, fault diagnosis and predictive maintenance plans, and displays system status and sends alarms through a user interface. According to the invention, intelligent power grid optimization, battery health state monitoring and fault diagnosis early warning are realized, and through multi-source data fusion, real-time artificial intelligence analysis and adaptive learning, the efficiency and reliability of the energy storage system are improved, and the service life of the energy storage system is prolonged. The system can predict the energy demand, evaluate the health state of the battery and identify an abnormal mode, thereby actively making a maintenance plan, reducing accidental shutdown, and providing a comprehensive solution for intelligent management of the energy storage system.

In one implementation, a device uses a large language model-based agent to perform a task in a network, to obtain a first result. The device executes code in the network to perform the task, to obtain a second result. The device makes a comparison between the first result and the second result. The device provides, based in part on the comparison, one or more performance metrics for the large language model-based agent for display.

The present invention relates to the field of building electromechanical engineering technology, and in particular to an intelligent comprehensive planning system for building electromechanical pipelines based on BIM and artificial intelligence. The technical solution adopted by the present invention is: including a multi-source data fusion module, building a data interface, seamlessly connecting CAD drawings, equipment parameter documents, construction site information and other multi-source data, and importing them into the BIM core database with one click; using a data cleaning algorithm to eliminate redundant and erroneous information, ensuring that the basic data of the model is accurate and reliable, and laying a solid foundation for subsequent pipeline planning; first, according to different types of buildings and different building spaces, classify and organize the electromechanical deepening design layout knowledge, and list all electromechanical systems and subsystems under the electromechanical system in the area. The advantages of the present invention are: it can reduce pipeline collisions by more than 90%, reduce rework costs by about 40%; shorten the construction period by more than 25%, and make construction resources more reasonably allocated; greatly reduce troubleshooting and maintenance time; and comprehensively improve the quality and benefits of building electromechanical engineering.

In one implementation, a device receives an output of a language model-based troubleshooting agent to perform a task with respect to a computer network. The device determines a level of quality of the output. The device generates an instruction for the language model-based troubleshooting agent, when the level of quality of the output is below a threshold. The device requests that the language model-based troubleshooting agent perform the task using the instruction.

The invention relates to a maintenance operation guiding method, system and equipment based on intelligent interaction, and the method comprises the steps: analyzing the operation intention of a user as at least one type of equipment dismounting, fault removal and knowledge retrieval, and generating a multi-mode guiding strategy; based on a multi-modal guiding strategy, coordinating and controlling a man-machine collaborative operation link; after one job is completed, multi-dimensional verification data is constructed, and when any one-dimensional verification data exceeds a corresponding knowledge graph threshold value, intervention work is activated; and generating a guiding efficiency file which is synchronously mapped with the operation link and comprises a multi-mode guiding strategy triggering log, user response behavior sensing data and a strategy adjustment utility evaluation graph, and binding the guiding efficiency file and the unique identification code of the maintenance object through an encrypted data link and storing the guiding efficiency file and the unique identification code in a preset database. According to the method, the talent cultivation period is remarkably shortened, meanwhile, the maintenance quality stability and the knowledge inheritance efficiency are synchronously improved, and reliable technical support is provided for equipment full-life-cycle management.

The invention relates to the technical field of robot control methods, in particular to a simulation-to-reality migration-oriented robot gait optimization method, which comprises the following steps of: acquiring input characteristics of a robot; constructing a gait control model by adopting a near-end strategy optimization reinforcement learning algorithm; training the gait control model in parallel in a physical simulation environment; the input feature matrix is input into the trained gait control model, a joint target position control instruction is generated, and the robot is controlled to execute gait actions; executing domain randomization operation by applying simulation to a reality checking tool; the simulation parameters are adjusted according to the difference parameters, and optimized gait control model parameters are output. The uncertainty of the real environment is covered by applying simulation to reality troubleshooting tool execution domain randomization operation, the systematized difference positioning process is combined, the simulation and real machine difference parameters are recognized, and the accuracy of the gait control model is improved. And action malformation, poor stability or hardware damage of the gait strategy of simulation training during real machine deployment can be prevented.

The invention discloses a server link fault positioning system and method, electronic equipment and a storage medium, and relates to the technical field of server link tests.Physical layer data and protocol layer data of a data collection point are collected through a collection assembly, a test assembly is controlled to conduct server link testing, the test assembly comprises a control module and an analysis module, and the control module is connected with the analysis module. The test instruction is issued to the server link through the control module, when the server link fault is monitored, the analysis module acquires the physical layer data and the protocol layer data of each data acquisition point from the acquisition assembly, and determines the fault position and / or the fault type of the server link according to the acquired data, so that the automatic link quality evaluation is realized, and the efficiency is improved. And the fault position and the fault type can be determined, so that the problems of difficulty in accurately positioning the fault position and distinguishing the hardware link fault and the software error through a single pressure test and low troubleshooting efficiency caused by manual fault positioning in the prior art are solved, and the technical effect of improving the troubleshooting efficiency is achieved.

The invention provides a multi-mode authority management and record tracing method and system for an intelligent lock. The method comprises the following steps: step 1, obtaining the intelligent lock, performing initialization setting on the intelligent lock, connecting the intelligent lock to a home local area network, and establishing secure communication connection with a cloud server; according to the invention, multiple identity recognition modes are supported, multiple permission types are provided, and a permission type combination strategy can be formulated, so that the flexibility of permission management is greatly improved, and diversified use requirements of modern families are met; according to the method, unlocking information of each time is automatically recorded, and a user behavior model is constructed in combination with a big data analysis technology, so that potential safety risks and abnormal behavior modes can be quickly and accurately identified, and the efficiency and accuracy of troubleshooting are improved; the intelligent lock can be quickly and automatically switched to the emergency mode when facing a complex environment or a special condition, so that the malicious attack resisting capability of the system is effectively improved, and the safety and the stability of the intelligent lock in the complex environment are enhanced.

The invention discloses an efficient processing method and system for online troubleshooting of the information department, and relates to the technical field of automatic operation and maintenance, and the system comprises a multi-modal data acquisition module, a dynamic knowledge graph construction module, a reinforcement learning driven self-healing module, an edge co-processing module and a cross-domain privacy calculation module. According to the method, the limitation of traditional single-dimensional monitoring is broken through, multi-source heterogeneous data such as server performance, container state and network flow are fused, a high-timeliness and high-consistency decision information base is constructed through unified time sequence alignment and semantic standardization, and a panoramic view is provided for dynamic analysis; based on an incremental learning mechanism and a causal inference engine, a service topology dependency relationship is updated in real time, a deep root cause is accurately positioned, and a cross-system and cross-level complex fault combination is solved.

The invention discloses a digital twinning-based police cloud Internet of Things equipment three-dimensional scene building method and system, and belongs to the technical field of digital twinning, and the method comprises the steps: laying sensing equipment in a target region; establishing a three-dimensional entity model of the target area in the cloud server, laying a corresponding virtual model in the entity model based on the installation information of the sensing equipment, and accessing data from the sensing equipment into the virtual model to generate a digital twin model; the first troubleshooting unit analyzes the scene video and the environment data collected by the sensing equipment in real time, and if an environment risk is detected, a disposal scheme is generated; the second troubleshooting unit processes the scene video into a multi-section video, analyzes the multi-section video in combination with environmental data, and determines suspicious personnel and risk degree of the target area; and screening suspicious persons whose risk degrees are greater than a first threshold value as high-risk risks. According to the invention, safety management personnel can pay attention to and deal with high risks in time, and the emergency response speed and accuracy are effectively improved.

The invention relates to the technical field of power converters, in particular to a power converter real-time fault diagnosis method and system based on edge calculation. According to the method, operation data, including electrical parameters, thermal parameters and the like, of the power converter are obtained firstly, aging and quality are ensured through synchronous filtering, limitation of a traditional single parameter is broken through, tiny abnormity and instantaneous fluctuation can be captured, and redundancy is avoided; according to the equipment structure, edge computing nodes are deployed near a data source, the transmission distance is shortened, delay is reduced, electromagnetic interference is avoided, accuracy is improved, resources with excellent computing power are matched, and local real-time monitoring is supported. Next, an edge computing node lightweight algorithm is used for localization analysis, normal data and abnormal data are quickly distinguished, identification time consumption does not need to be reduced by transmitting to a cloud end, it is ensured that abnormality is found immediately according to misjudgment and sensitivity improvement of a dynamic baseline, and finally, thermal radiation information is obtained in combination with abnormal data, and a fault is accurately positioned by associating a device structure and parameters. And refining to an element level to shorten the troubleshooting time, and analyzing the trend to realize predictive maintenance.

The invention provides a 5G portable WiFi fault diagnosis and recovery method and system. The method comprises the following steps: collecting network connection data, system information and operation logs from a communication terminal connected to 5G portable WiFi equipment; analyzing the data, and generating a fault diagnosis report; selecting and executing corresponding recovery operation from a recovery scheme library according to the report; monitoring equipment state and network connection in real time, and judging whether recovery is successful or not; if the fault is not solved, an advanced solution is provided, and equipment information, program attributes, scheme library information, network connection data, system information and operation logs are sent to an edge server; the edge server obtains fault data and diagnosis reports of a plurality of similar devices as references, potential fault laws are mined through big data analysis, and reference analysis data are generated; and optimizing an intelligent diagnosis program and a recovery scheme library according to the reference analysis data and other information. The fault type can be accurately identified, the fault recovery time is remarkably shortened, and the user experience is improved.

The invention discloses a new energy equipment troubleshooting method based on a knowledge graph and a large model. The method is used for solving the problems of fault positioning and troubleshooting of photovoltaic and wind power new energy equipment and the like. The method mainly comprises the following steps: performing data cleaning and preprocessing on a new energy equipment troubleshooting professional field document, and constructing a structured knowledge graph of equipment types, fault types, fault information and solution nodes in a graph database through Cypher; the big language model analyzes the user fault description, extracts and maps node attributes in the knowledge graph, and generates a graph query statement; the question-tracing type multi-round interaction is realized, the missing information is dynamically complemented, and the basic fault is accurately positioned in a step-by-step reasoning mode; according to the method, multi-modal resources such as texts, schematic diagrams and videos stored in the knowledge graph are combined, a step-by-step reasoning path and a solution with illustrative features are generated through a preset template, the whole reasoning process is visualized in a branch thinking graph form, and the accuracy, interpretability and user experience of troubleshooting are remarkably improved.

The invention provides a knowledge graph-based IT operation and maintenance fault tracing method and device, and the method solves the problem of low fault positioning efficiency in a high-complexity heterogeneous network through fusing a topological path and a knowledge graph of a fault propagation mode, and specifically comprises the steps: firstly, carrying out the fusion of the knowledge graph, and carrying out the positioning of a fault through the fusion of the knowledge graph; the incidence relation between the physical connection and the fault logic is represented, so that the reduction accuracy of a cross-equipment fault propagation path is improved; secondly, manual troubleshooting participation degree is reduced through semantic similarity analysis, and priority ranking of fault roots is achieved in a complex network environment; and finally, the reliability of fault positioning is guaranteed through consistency verification, and misjudgment caused by simple rule matching is avoided.

In one implementation, a device configures an impairment scenario in a computer network. The device performs an evaluation of the impairment scenario by a language model-based troubleshooting agent. The device makes a comparison between a root cause associated with the impairment scenario and the evaluation of the impairment scenario by the language model-based troubleshooting agent. The device provides an indication of the comparison.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention relates to a signal equipment fault diagnosis method and device, equipment and a medium, and the method comprises the following steps: carrying out the systematic processing of project data, constructing a project-level knowledge graph, and carrying out the fine adjustment of a first large model; performing systematization processing on product requirements and design files, constructing a product-level knowledge graph, and performing fine adjustment on the second large model; when fault information of the signal equipment is obtained, sending the fault information to the first large model; the first large model carries out fault diagnosis, and if the first large model can output an accurate fault reason, the fault reason is fed back to a user; otherwise, downloading the log in the specified time period, and sending the fault information and the equipment log to the second large model; and the second large model generates a retrieval code through an intelligent agent according to the fault information and the equipment log in combination with product-level knowledge, analyzes related functional variables in the log, realizes fault diagnosis, outputs fault causes and generates a fault report. Compared with the prior art, the method has the advantages of being high in reasoning accuracy, improving the fault diagnosis speed and the like.

The embodiment of the invention relates to the technical field of alarm processing, and provides an equipment fault alarm processing method and device, electronic equipment and a medium, and the method comprises the steps: obtaining current equipment fault alarm information; inputting the current equipment fault alarm information into a pre-trained equipment fault alarm prediction model, analyzing the equipment fault alarm information, and determining a target fault category corresponding to the equipment fault alarm information; determining a target alarm level corresponding to the target fault category based on a preset fault alarm level; and estimating a target fault reason based on the equipment fault alarm information and the target alarm level to generate a troubleshooting guidance scheme. Therefore, alarm data are automatically analyzed through the pre-trained artificial intelligence large model, automatic identification of fault equipment, intelligent inference of fault reasons and intelligent recommendation of a troubleshooting method are realized, the efficiency and accuracy of alarm processing are improved, the workload of operation and maintenance personnel is reduced, and the reliability and service quality of a network are improved.

The invention relates to the technical field of industrial data, discloses a mining equipment state monitoring method and system based on the Internet of Things, and effectively solves the problem of insufficient model generalization ability caused by scarcity of fault samples of mining equipment. The virtual sample generation technology expands the available training data volume by 3-5 times, so that the early fault detection rate is improved to 85% or above. The self-adaptive feature selection mechanism reduces the consumption of computing resources by more than 30%, and maintains the integrity of key fault features at the same time. The multi-stage early warning system realizes accurate grading of fault severity, so that the maintenance resource distribution efficiency is improved by about 40%. The closed-loop optimization mechanism enables the model to continuously evolve in the operation process, and the annual false alarm rate is reduced by about 15%. The explainable diagnosis report provides a clear technical basis for field maintenance, and the average troubleshooting time is shortened by about 50%.

The invention relates to the field of fault diagnosis, and discloses a drying machine fault diagnosis method based on deep reinforcement learning, which is used for improving the accuracy and control efficiency of drying machine fault diagnosis. A physical dynamic model fusing thermodynamics and mechanical dynamics is constructed, real-time quantitative evaluation is performed on system stability in combination with a Lyapunov function, and a fault propagation path is visually displayed by using a directed topology network. Firstly, dryer multi-source parameters are obtained to establish a dynamic model; positioning a fault source component and calculating the network influence degree of the fault source component; and a self-adaptive control instruction and a topology suppression instruction are generated, control parameters are dynamically adjusted, fault propagation is suppressed, the control instruction is converted into an actuator driving signal, and accurate control over the drying machine is completed. Through deep fusion of a physical model and data driving, the fault diagnosis accuracy and the control efficiency are remarkably improved, the troubleshooting time is effectively shortened, the production safety is guaranteed, and the method is suitable for intelligent operation of the drying machine under the complex working condition.

The invention relates to an electric power informatization system fault cause analysis method, which comprises the following steps: step 1, in an offline stage, constructing a fault analysis decision tree according to an electric power business system document and knowledge and experience of operation and maintenance personnel, and registering various API tools required to be used in a diagnosis process; 2, in an online stage, generating a fault description according to a fault occurring in the system, then matching the fault analysis decision tree in the step 1 according to a keyword in the fault description, traversing the fault analysis decision tree by a control program, and performing fault analysis on the fault analysis decision tree; and combining the fault description, the execution decision of the nodes in the tree, the callable API tool and the historical fault information into a cue word template, and sending the cue word template to the large model, so that the large model analyzes the root cause of the system fault. According to the invention, the troubleshooting and positioning efficiency of the system can be improved.

In one implementation, a device performs, using a using a large language model-based troubleshooting agent, troubleshooting of a plurality of issues in a network indicated by a plurality of support tickets opened by users of the network. The device aggregates, based on results of the troubleshooting, the plurality of support tickets into a master support ticket. The device obtains a resolution to the master support ticket from a support engineer. The device uses the resolution to the master support ticket in conjunction with the large language model-based troubleshooting agent to troubleshoot a new support ticket.

The invention discloses a two-stage multi-feature-based distribution and storage user identification method, which overcomes the problems of high cost, low efficiency and poor identification precision of distribution and storage user identification in the prior art, and comprises the following steps: clustering users by using a kmeans algorithm and determining a potential distribution and storage user group in combination with a custom rule; carrying out multi-dimensional typical distribution and storage user load feature and energy consumption feature extraction on the potential distribution and storage users based on the preliminary identification result; based on the extracted multiple features, a machine learning classification model is adopted to carry out accurate identification training on the distribution and storage users; and utilizing the trained machine learning classification model to identify the storage allocation user. Through a two-stage algorithm, end-to-end classification identification is directly realized based on the power grid side data, the accuracy of distribution and storage user identification is improved, a large amount of manpower troubleshooting is not needed, the manpower cost and the troubleshooting time cost are greatly reduced, and the user distribution and storage identification accuracy is high.