137 results about "Exception analysis" patented technology

The invention discloses an equipment fault early warning method and device, equipment and a storage medium, relates to the technical field of equipment data processing, and discloses the following steps: obtaining operation data of target industrial equipment and associated data of each operation data; performing exception analysis according to the association degree between the operation data and the associated data to obtain exceptional data; performing graph correlation analysis on the abnormal data according to a preset fault graph to obtain a fault type correlation degree; determining an early warning level and a prediction fault type according to the fault type association degree; performing fault early warning on the target industrial equipment based on the early warning level and the predicted fault type; according to the method, various sensor data of industrial equipment are collected, abnormal data atlas analysis is carried out based on relevance of data when different faults occur in a fault atlas, a root fault reason is recognized, then the relevance degree of different fault types is quantified, the fault types are determined, and the early warning level is determined. The accuracy and efficiency of fault identification are improved, and equipment maintenance is carried out in advance based on potential fault problems.

The invention belongs to the technical field of power grid main equipment diagnosis, and provides a power grid main equipment fault diagnosis method and system based on voiceprint cloud edge collaboration, which adopts a new cloud edge collaboration method and divides the analysis of voiceprint monitoring data into two stages. According to the method, the exception detection model is deployed on the edge side, exception analysis of mass voiceprint data is achieved, concurrent requests of a large number of concurrent signals at the cloud side are avoided, and the timeliness of voiceprint monitoring and analysis is improved. In the cloud, a multi-modal defect identification method fusing voiceprint, visible light, infrared and other multi-source data is deployed, secondary classification of sound abnormal signals is realized, and the reliability of equipment operation, maintenance and management is improved.

The invention relates to the technical field of solid state disk performance evaluation. The solid state disk performance evaluation method comprises the steps of obtaining solid state disk operation information, conducting exception analysis on the solid state disk operation information through a deep learning model to obtain solid state disk exception information, conducting correction and interpolation on the solid state disk exception information, and obtaining the solid state disk performance evaluation result. Performing feature extraction on the corrected operation information to obtain a plurality of performance features of the solid state disk, screening the plurality of performance features based on a feature selection method to obtain a target feature set required by performance evaluation, and analyzing the target feature set required by performance evaluation through a time sequence prediction model. Obtaining performance change trend information under different loads, predicting the residual life and the performance degradation condition of the solid state disk according to the performance change trend information, and obtaining a performance evaluation result. The method has the effect of improving the performance evaluation accuracy of the solid state disk.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The embodiment of the invention provides an exception analysis method, electronic equipment, a storage medium and a program product, and relates to the field of large language models.The method comprises the steps that in response to an exception of a target monitoring task, target exception information of the target monitoring task within the exception occurrence time range is obtained, and obtaining target exception analysis data matched with the target exception information from a preset knowledge base, and obtaining exception analysis result data determined by the large language model according to the target exception analysis data and the target exception information. Wherein the preset knowledge base comprises exception analysis data of a plurality of monitoring tasks in at least one task chain. According to the technical scheme, anomaly analysis can be accurately and efficiently achieved, and the requirement of a large-scale time sequence calculation task for real-time performance is met.

The invention discloses an electric power communication analysis system and method based on big data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the sensitivity grading, and generating labels; gathering the data to a big data platform, and completing storage, partitioning and index construction; an asynchronous main control chain mechanism is constructed, high-optimal data is dispatched for rapid anomaly judgment, and other data is processed in parallel; performing desensitization mapping on the high-sensitivity data to generate a short-time mapping bill and performing desensitization analysis; issuing a local task for the low-confidence exception, collecting backflow data, and verifying and determining a high-risk event; restoring the sensitive data in the authorization window, and destroying the bill and the secret key after abnormity analysis is completed; and triggering network regulation based on an analysis result, and recording the whole process to an audit log. By introducing an asynchronous scheduling and reversible desensitization mechanism, high-efficiency anomaly analysis and whole-process traceability of the electric power communication big data on the premise of ensuring safety and compliance are realized.

The invention relates to the technical field of ERP (Enterprise Resource Planning) data processing, in particular to an ERP data exception analysis method and system based on big data. Extracting time sequence features and frequency domain features of the ERP potential abnormal data, inputting the time sequence features and the frequency domain features into a fluctuation detection model, carrying out fluctuation recognition on the ERP potential abnormal data to obtain ERP fluctuation data, and carrying out multi-dimensional linkage analysis on the ERP fluctuation data through a covariance analysis method to obtain linkage strength among different types of ERP fluctuation data; according to the linkage strength and the time sequence characteristics and the frequency domain characteristics of the ERP potential abnormal data, a service category target threshold value is obtained through a dynamic threshold value adjustment model, abnormal judgment is conducted on the ERP fluctuation data according to the service category target threshold value, when the ERP fluctuation data exceed the target threshold value range, the ERP fluctuation data are marked as first target abnormal data, and a data abnormal early warning mechanism is triggered. And more accurate data anomaly analysis is realized.

The embodiment of the invention provides an information processing method and device, and the method comprises the steps: obtaining an application exception index of a target application; extracting application exception information corresponding to the target application according to the application exception index, performing information processing on the application exception information in at least one exception analysis dimension, and determining an application exception type of the target application; creating an application repair strategy for the application exception type, and determining application code information corresponding to the target application; and inputting the application repair strategy and the application code information into a code repair model for processing to obtain an application repair code, and performing exception repair on the target application by using the application repair code.

The invention discloses an anomaly analysis method and device, a storage medium and electronic equipment, and the method comprises the steps: firstly obtaining a plurality of lead electrocardiograms of a user, determining a first feature of the electrocardiograms through a feature extraction model, and extracting a second feature of the electrocardiograms according to a preset to-be-extracted feature parameter; and obtaining a fusion feature based on the first feature and the second feature. And finally, inputting the fusion features into an exception analysis model to obtain an exception analysis result of the user output by the exception analysis model. According to the method, the model with the small parameter quantity is used for carrying out feature extraction and outputting the anomaly analysis result, the second feature obtained through the preset to-be-extracted feature parameter is fused with the first feature extracted by the model to carry out feature supplementation, and then the accuracy of the anomaly analysis result is improved. By means of the feature supplementing mode, the accuracy of the abnormal analysis result is guaranteed, the energy consumption of the model is low, and therefore the use durability of the portable electrocardiograph is improved.

The invention discloses an application debugging method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining debugging real-time streaming data corresponding to an application debugging process, and detecting whether the debugging real-time streaming data is abnormal or not; if yes, extracting real-time stream abnormal data in the debugging real-time stream data, and analyzing the real-time stream abnormal data according to a preset knowledge base to obtain an abnormal analysis prompt word; inputting the exception analysis cue word into a real-time data processing full-stack agent to obtain an exception problem summary, an exception analysis reason and an exception repair suggestion output by the real-time data processing full-stack agent; and debugging the application debugging process based on the exception problem summary, the exception analysis reason and the exception repair suggestion. According to the method, the development threshold is reduced through the intelligent agent, the knowledge base and the like, so that error checking and problem repairing in the application debugging stage are accelerated, meanwhile, dependence on experts is reduced, and the development efficiency of the real-time data processing application is improved.

The invention relates to the technical field of service management, in particular to a rule engine dynamic optimization method and system based on real-time service requirements, and the method comprises the steps: obtaining an initial service index in a service scene, constructing a first test parameter set according to the initial service index, operating a test, and collecting first operation data; in a preset time window, continuously monitoring the initial business indexes, fusing dynamic factors in the continuous monitoring process into the first test parameter set to generate a second test parameter set, running the test again, and collecting second running data; performing difference quantitative analysis on the first operation data and the second operation data to obtain a rule operation deviation vector; inputting the engine exception data into a preset rule engine exception analysis model to obtain an engine exception score; in response to the fact that the engine exception score exceeds a preset exception threshold value, the continuous operation duration of an existing rule engine is obtained, the continuous operation duration and the engine exception score are input into the rule engine optimization model, and a rule engine optimization strategy is obtained.

The invention discloses an intelligent accounting management system and method for business travel reimbursement based on a cloud platform, and relates to the technical field of business travel reimbursement supervision, and the system comprises a feature processing module, an anomaly analysis module, and a reimbursement processing module. The feature processing module is used for collecting feature parameters of each consumable record and converting attribute type parameters into numeric type parameters through encoding processing to train a travel reimbursement abnormal probability model; the anomaly analysis module is used for monitoring and collecting consumption records when the user is on a business trip in real time, screening the consumption records when the user is on the business trip, and analyzing the anomaly probability and the overlap ratio of each reimbursement record; and the reimbursement processing module performs type division on each reimbursement record through threshold judgment, then performs reimbursement processing and feedback, and analyzes an auditing priority evaluation value of the manual auditing record.

The invention relates to the field of data processing, text generation technologies, large model technologies and large language model technologies, and discloses a model evaluation method and device, computer equipment, a storage medium and a program product.The model evaluation method comprises the steps that a first query statement generated by a to-be-evaluated model language model is obtained; performing exception analysis on a target field matched with a preset evaluation index in the first query statement to obtain exception information difference information; and performing clustering analysis on the abnormal information difference information based on the preset abnormal type and the preset difference type to obtain an evaluation result of the language model of the to-be-evaluated model. According to the method and the device, the difference information of the first query statement generated by the language model can be subjected to clustering analysis based on the preset difference type to obtain the evaluation result corresponding to the language model, so that the carrying of the language model can be known through the evaluation result, and the iteration efficiency and accuracy of the language model are improved.

The invention discloses a cloud platform network equipment strategy management method, which comprises the following steps: acquiring demand data of a current monitoring time node and a network equipment strategy, performing association analysis on multiple pieces of demand data, and determining multiple associated demand data sets according to an analysis result; matching the network equipment strategies in each associated demand data set, judging whether an exception exists or not according to a matching relationship, if so, determining an exception type and an exception fragment, constructing an exception fragment set, and determining a plurality of optimization strategies of the exception fragment according to a preset exception analysis model; obtaining a plurality of simulation network equipment strategies based on the plurality of optimization strategies, generating a risk assessment value according to a preset risk assessment model, and selecting an optimal simulation network equipment strategy and an optimal optimization strategy; and according to the optimal optimization strategy, generating an actual network equipment strategy and issuing a management instruction, thereby effectively solving conflicts between network equipment strategies, providing security equipment strategy optimization suggestions, and ensuring security and stability of a network environment.

The invention relates to the technical field of claim settlement operation data processing, in particular to a claim settlement operation platform digital decision-making system based on a large model Agent. The data processing module comprises an associated data determining unit used for determining associated data in the business data; the data setting unit is used for determining setting of the business data; the exception analysis unit is used for carrying out exception analysis on the business data; the data cleaning unit is used for extracting abnormal data and normal data in the business data and cleaning the abnormal data and the normal data; the data adjusting unit is used for adjusting the service data setting; and the adjusting module is used for adjusting the model or the data processing process. The problem of low claim decision making efficiency caused by low claim decision making accuracy in the prior art is solved.

The invention provides a coal mine environment abnormity monitoring system and method based on machine learning, and the system comprises a data access module which is used for accessing multi-dimensional monitoring data of a coal mine environment in a monitoring center; the configuration module is used for establishing and configuring an abnormal rule in the monitoring center; and the exception monitoring module is used for performing exception detection on the multi-dimensional monitoring data according to the configuration result and based on machine learning, and outputting an exception data point and an exception analysis report. Multi-dimensional monitoring data of a coal mine environment is accessed into a monitoring center, and abnormal rules are effectively established and configured in the monitoring center, so that abnormal detection can be performed on the multi-dimensional monitoring data according to a configuration result and based on machine learning, and abnormal data points and abnormal analysis reports are output; according to the method, single-variable abnormity can be detected, abnormity analysis can be carried out by integrating data of multiple dimensions, abnormity reason analysis is provided, and managers are helped to rapidly identify potential risks and take measures.

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.

The invention discloses a log processing method and device, equipment and a medium. The method comprises the following steps: acquiring a log buffer area corresponding to each application program in a vehicle; wherein the log buffer area comprises a main buffer area and a backup buffer area; if the abnormal event of any application program is detected, executing abnormal response logic on the log buffer areas of all the application programs based on the abnormal event to obtain global log data of the abnormal event and storing the global log data; and when detecting that an exception analysis condition is met, performing exception analysis according to the stored global log data. According to the technical scheme, the log data among the application programs can be comprehensively captured when any application program is abnormal, so that the global log data is obtained, the real-time performance and integrity of log data capturing are improved, and abnormity positioning and fault analysis are facilitated.

The invention discloses a business system development management system based on big data analysis, particularly relates to the field of business system development, and comprises a data acquisition time determination module, a development data entry module, a development data analysis module, an exception analysis module and a management module. According to the invention, by integrating a big data analysis technology, the efficiency and accuracy of business system development management are significantly improved. All the modules work cooperatively, closed loop of data acquisition, analysis, anomaly detection and automatic management is realized, comprehensive, real-time and accurate business data is ensured, a system state is scientifically evaluated by a comprehensive anomaly index model, potential problems are timely found and early warned, powerful support is provided for management decision, and meanwhile, the management efficiency is improved. And an emergency response mechanism ensures that the system can be quickly recovered when the system is abnormal, the service continuity is ensured, and on the whole, the controllability and stability of service system development are greatly enhanced.

The invention discloses a fault self-check recovery method and system based on a cloud deployment system, and the method comprises the steps: carrying out the multi-dimensional deep detection of the operation states of an operation system layer, a database layer and an application layer of a cloud platform through constructing an all-dimensional real-time monitoring system, and automatically triggering an adaptive recovery mechanism once an abnormality is found, and continuous and stable operation of the system is ensured. The matching tool integrates core modules of state acquisition, anomaly analysis, recovery execution, log recording and the like, collaboratively realizes real-time acquisition of operation data, accurate anomaly identification through an intelligent algorithm, rapid matching and implementation of a recovery scheme, and records logs in the whole process for troubleshooting and system optimization. According to the tool, server nodes, database states, service accessibility and webpage availability are checked in sequence, emergency processing is started immediately when abnormity is found, the stability and reliability of a cloud platform are remarkably improved, and the manual intervention cost is reduced.

The invention provides a hard disk exception real-time monitoring method and system, and relates to the technical field of storage monitoring, and the method comprises the steps: obtaining the operation data of a hard disk in real time through a preset monitoring system, and outputting the original data; preprocessing the original data by using a preset preprocessing method, and outputting to-be-analyzed data; performing feature extraction on the to-be-analyzed data, selecting historical operation data matched with the to-be-analyzed data from a historical database based on the extracted features, and training and optimizing a preset model based on the historical operation data to obtain an anomaly monitoring model; and performing exception analysis and prediction analysis on the to-be-analyzed data by using the exception monitoring model, outputting an exception-prediction analysis result, and obtaining an exception coping strategy matched with the exception-prediction analysis result in combination with a preset result-strategy comparison table. According to the method, the running state of the hard disk can be monitored and predicted in real time, potential hard disk problems can be found and processed in time, and the stability of the system and the safety of data are guaranteed.

The invention relates to a computing power anomaly detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring abnormal heterogeneous computing power task data and heterogeneous computing power task detection data; determining abnormal computing power container information and abnormal hardware node information from the abnormal heterogeneous computing power task data; performing software exception analysis processing on the basis of the exception computing power container information to obtain software dimension exception information, performing hardware exception analysis processing on the basis of the exception hardware node information to obtain hardware dimension exception information, and comparing the heterogeneous computing power task detection data to obtain first exception detection information and second exception detection information; therefore, the calculation power anomaly detection rate is determined. According to the method, the detection rate of the heterogeneous computing power task detection data in the heterogeneous computing power task execution process can be determined, so that the stability of the computing power service is effectively improved through detection rate analysis.

The invention discloses an enterprise business application operation and maintenance situation awareness system, and belongs to the technical field of business operation and maintenance, and the system specifically comprises a business topology construction module which is used for collecting inter-service call logs in real time through a data collection agent disposed on a business application server; the exception propagation analysis module is used for receiving the real-time dependency relationship graph generated by the service topology construction module, and the dependency relationship graph is stored in a graph database in a digraph structure; the visual display module is used for reading the current topological state and the anomaly analysis result from the graph database, nodes and edges are rendered by adopting a force-oriented layout algorithm, the node radius is in direct proportion to the resource occupancy rate, and the edge width is in inverse proportion to the calling success rate; according to the invention, efficient perception and accurate presentation of the operation and maintenance situation of the enterprise business application are realized, and the operation and maintenance efficiency and decision accuracy are improved.

The invention discloses an information security detection method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring user access information and data access abnormal information in a to-be-detected data access period; performing anomaly analysis on the user access information to obtain a user access information analysis result; performing exception analysis on the data access exception information to obtain a data access exception information analysis result; and based on the user access information analysis result and the data access abnormal information analysis result, performing information security detection on the to-be-detected data access time period to obtain a target information security detection result. According to the method and the device, the abnormal access behavior of the user and the abnormal condition of data access can be effectively detected, so that the information security in the data access process is comprehensively detected.

The application belongs to the technical field of photovoltaic power generation equipment fault monitoring, and discloses a kind of distributed photovoltaic power generation equipment intelligent monitoring system, including light intensity equipment exception monitoring module, control equipment exception monitoring module, drive equipment exception monitoring module, tracking system exception analysis module, support structure data acquisition module, support structure exception analysis module and fault cause pointing identification module.The application analyzes the abnormal conditions of tracking system and support structure, and then comprehensively evaluates the health status of each automatic tracking photovoltaic power generation device.This analysis method takes into account the tracking system and the support structure, covering the key aspects of device operation and support, which greatly improves the accuracy of evaluation, accurately grasps the real situation of the equipment, discovers potential problems in time, formulates more targeted maintenance strategies, effectively ensures the stable operation of photovoltaic power generation devices, improves power generation efficiency and reduces equipment failure risk.

The invention relates to the technical field of computers, in particular to an application exception handling method, system and equipment based on agent arrangement, which can efficiently handle application exception problems. The application exception handling method based on agent arrangement comprises the steps that by means of agent arrangement, an exception handling workflow comprising a plurality of agent units capable of being arranged is formed in a visual mode for an application exception condition, and a flow engine of the exception handling workflow comprises a control node and a task node; for the captured application exception information, exception information features are extracted through exception analysis logic by using an exception recognition agent unit, and the influence range of the application exception is delimited through an entity relationship graph to form an exception analysis result to be output to a front-end visual interface or pushed to a collaborative agent unit; and establishing an agent co-processing system comprising a co-processing agent unit based on the exception analysis result, matching a processing scheme for the application exception information according to a co-processing strategy, and processing the exception condition.

The invention relates to the technical field of software running exception analysis, and discloses an AI-based software running exception analysis method and system, performance information and text information during software running are collected and matched, a judgment value P is calculated according to the performance information, and when the judgment value P is 0 or a negative number, the software running exception is analyzed. The duration data SJ and the occurrence frequency data CS are extracted for judgment, when a judgment value P is a positive number and the judgment value P is larger than or equal to a fault threshold GY, a detection instruction is sent, when the instruction is sent, the fault problem is analyzed, the fault position is positioned, a repair strategy is called, and fault isolation and repair are carried out; according to the method, the performance information and the text information during software operation are acquired, so that when the software operation has a problem, the fault can be known, and the fault can be found and processed in time in the early stage, and the situation that the software is directly and abnormally broken is avoided.

The invention relates to the field of big data, and provides an anomaly detection and repair method and system for a big data task and a medium. The big data task anomaly detection and repair method comprises the steps that task information of a big data task with an abnormal task state is obtained, wherein the task information comprises a task log; obtaining an execution code of the big data task through a decompilation tool, and analyzing the execution code and the task log through an exception analysis module to identify an exception problem; in response to the fact that the exception problems are data source exception and code exception, exception code snippets, data source information and exception types are analyzed through a conjoint analysis module to determine exception root causes, the exception code snippets are recognized through an exception analysis module, the data source information is determined by executing codes, and the exception types are determined according to task logs; and triggering abnormal code repair and / or data processing optimization according to the abnormal root cause.

The present application provides an abnormal analysis method, device and storage medium, including: obtaining an abnormal data sequence generated by an abnormal event; using a preset clustering model to classify the abnormal data sequence to obtain at least two categories of abnormal sequences; the preset clustering model is used to obtain the abnormal type of the abnormal data sequence; performing association mining on at least two categories of abnormal sequences to obtain at least one association rule; associating at least one association rule to construct an abnormal association relationship; locating the cause of the abnormal event according to the abnormal association relationship. By classifying the anomalies and mining the association rules of the anomalies to obtain the association relationship between the anomalies, the cause of the abnormal event is located, and the clustering model can be maintained in real time to avoid feature failure, which improves the mining effect of anomalies without propagation relationship, and achieves rapid and accurate positioning of the cause of the abnormal event.

The invention provides an exception analysis method and system and a storage medium, and the method comprises the steps: constructing an exception large model and an exception knowledge base in a cloud in advance, receiving exception data uploaded by a vehicle terminal in real time, and when exception type information exists in the exception data, matching the exception data according to the exception knowledge base, obtaining a final abnormal conclusion; when the abnormal type information does not exist, inputting the abnormal data into an abnormal large model for reasoning, and matching the abnormal data by adopting the abnormal knowledge base based on a reasoning result to obtain a final abnormal conclusion; and issuing the final abnormal conclusion to the vehicle terminal. According to the method provided by the invention, the technical problems of slow system operation, large resource occupation and dead halt caused by abnormal data of the vehicle-mounted terminal are effectively solved, and the safety and reliability of the vehicle-mounted terminal system are effectively improved.