21 results about "Failure causes" patented technology

This application relates to an SMT (Surface Mount Technology) material delivery scheduling method and system. It includes: executing a material delivery task; when reading the identification code of a material tray fails, identifying the failure type as optical overexposure, and reporting the failure information to a central scheduling system; the central scheduling system updates the execution history information of the material delivery task based on the failure information; when the central scheduling system needs to allocate an automated guided vehicle (AGV) to the material delivery task, it queries the execution history information of the material delivery task; if the query results indicate that the material delivery task has failed, it identifies the type of AGV that caused the failure in the execution history information, excludes the AGV type that caused the failure from the available AGVs, and selects AGVs with different physical configurations for allocation. This solves the problem of code reading failure caused by optical overexposure of high-gloss material trays in the SMT production environment.

The present invention addresses the problem in that when AI training is performed on the basis of the work record of a low-skilled worker, erroneous inference results are obtained for a failure cause and a countermeasure plan.　A representative failure cause analysis device according to the present invention comprises: a work record storage unit that stores work records including a failure event that has occurred, a failure cause, and information about a countermeasure work for the failure event; a work record selection unit that selects, from the work records stored in the work record storage unit, a work record to be used for training a generative AI on the basis of a predetermined condition; and an inference unit that trains the generative AI for a newly occurred failure event on the basis of the work record selected by the work record selection unit, and infers a failure cause and a countermeasure work for the newly occurred failure event using the trained generative AI.

The present application relates to model information processing technical field, especially a kind of equipment inspection method and system based on space intelligence, its method is provided with to generate inspection queue associated chain, when generating associated chain, periodically collect several operating data or several fault data of each equipment, and record according to collection period;According to the connection relationship of each equipment, the basic associated chain corresponding to each equipment is formed. Through the grouping of equipment, the associated equipment is synchronously inspected, which avoids the problem that the traditional inspection method cannot determine the equipment failure caused by system problems, at the same time, the equipment is associated by using merging semantics, which avoids the problem of missing equipment operation or equipment failure caused by subjective judgment, thereby effectively determining the mapping relationship between equipment entity and digital model system, at the same time, by using the above method, the inspection queue of equipment can be adjusted through the connection between equipment systems, thereby effectively improving the accuracy of building equipment inspection.

The application discloses an elastic safety adaptive fault-tolerant control method and system for an uncertain nonlinear system, aiming at the composite problem of state jump and actuator failure caused by pulse FDI attack, proposes a pulse hybrid dynamic model, and constructs a hybrid regulator function accordingly. Through nonlinear coordinate transformation and the regulator function, the original constrained system can be converted into an unconstrained system, and the safety problem can be converted into a stability analysis problem. The actuator failure factor and deviation are estimated to offset the fault influence, an event-triggered fault-tolerant control law containing adaptive fault compensation is designed, and the communication load is reduced through an event-triggered mechanism. Lyapunov analysis proves that the system meets the safety constraint and is asymptotically stable under the double threats, effectively solves the problem of simultaneously coping with state jump, fault and hard constraint, and significantly enhances the system elasticity and fault tolerance.

This invention provides a front-end testing system and its control method based on browser debugging and AI generation. The system includes an input module, an automated control adaptation layer, a debugging protocol collector, a test evidence package builder, a test case generation orchestrator, an AI code generation engine, a script quality gate, and a self-healing and backfeeding module. The self-healing and backfeeding module is data-connected to the script quality gate to determine the failure reasons of failed test script drafts and to use corresponding strategies to correct the failed test script drafts, effectively improving test case construction efficiency. The test intent structure, structured evidence package information, and test constraint template drive the AI ​​code generation engine to generate test script drafts, reducing reliance on manual writing and positioning, as well as waiting for experience, shortening the path from requirement documents to executable test cases, reducing manual dependence, and improving bridging stability.

The present specification provides an exception diagnosis method, device, storage medium and product for automated testing. The exception diagnosis method comprises: obtaining a test log corresponding to a test case that fails to execute; inputting the test log into a classification model for analysis to determine a failure cause type of the test case; if the failure cause type is a code defect type, obtaining a multi-dimensional context related to the test case, the multi-dimensional context comprising a code context, a requirement context and a runtime context, the code context being used to describe code structure information corresponding to the test case, the requirement context being used to associate a functional requirement corresponding to the test case, and the runtime context being used to reflect a running state when the test case is executed; inputting the multi-dimensional context into a diagnosis model for analysis to obtain a diagnosis result, the diagnosis result at least comprising a location of an abnormal code and a failure cause description.

This invention relates to a software defect localization and repair recommendation method based on code semantic vectors, comprising: acquiring the source code, failure execution information, and preset failure explanation conditions of the software to be tested; the preset failure explanation conditions including vector matching thresholds and stage order relationships corresponding to the failure input entry stage, the abnormal semantic generation stage, and the failure result formation stage; dividing the source code into multiple code segments, generating code semantic vectors for each code segment, and generating failure semantic vectors based on the failure execution information; determining a candidate code segment set based on a similarity threshold; performing responsibility shrinkage processing according to a preset removal order, deleting unnecessary segments and retaining necessary segments, identifying the retained code segments as defect localization results, and generating repair recommendations based on their code semantic vectors. This invention can reduce irrelevant repairs or superficial matching repair suggestions, improve the consistency between repair recommendations and actual failure causes, and improve the efficiency of software defect repair.

A method and a system for analysing an equipment for current cycling test is disclosed. A processor receives an input dataset corresponding to the equipment. The input dataset includes a set of test control parameters, specification data, historical cycling test data and a set of setup parameters. The equipment is analysed based on the input dataset using an artificial intelligence (AI) model. The analysis is based on a validation of the specification data, the set of test control parameters and the set of test setup parameters with respect to the historical cycling test data. An outcome of the current cycling test is predicted based on the analysis as one of failure or pass. Upon predicting the outcome as failure, a reason of failure is determined by prompting a generative AI model.

This application discloses an eFuse programming control method, programming controller, and system, relating to the field of data processing technology. The method includes: when a programming command is received, acquiring attribute data from the eFuse's current operating environment; determining whether the attribute data is within a corresponding preset safety attribute range; and when the attribute data is determined to be within the preset safety attribute range, executing the programming operation according to the programming command. Environmental attribute data is introduced as a pre-judgment condition. This acts as a security checkpoint for the programming process, allowing execution only when the environment meets the standards, effectively preventing unreliable operations on the eFuse under unknown or unstable conditions that could potentially cause abnormal impacts on critical data. By pre-setting safety attribute ranges, hardware failures caused by external factors such as power fluctuations and electrostatic interference can be avoided, extending the lifespan of the eFuse and surrounding circuits. This significantly reduces the risk of miswriting, missed writing, or data bit flipping.

The present invention relates to diagnosing a failure of and providing maintenance information about an ATM. When a failure occurs in the ATM, a node at which an abnormal sensor value is initially measured during a failed transaction is identified on the basis of sensor values measured by various sensors provided in the ATM. A failure cause providing module that provided an abnormal operation cause for the failure is predicted and information about a failure occurrence module in which the failure actually occurred and information about the failure cause providing module are provided to a maintenance worker on site, supporting the maintenance worker so that the maintenance worker can, during a failure recovery process, repair the failure occurrence module along with the failure cause providing module that caused the failure.

The present disclosure relates to a failure cause analysis method, device and equipment of a target detection algorithm, and belongs to the technical field of target detection. The method comprises: training and / or testing the target detection algorithm through a data set, and obtaining failure data of the target detection algorithm in the training and / or testing process, each piece of failure data comprising a failure type and measurement values of a plurality of failure influence factors; performing causal reasoning on the failure process of the target detection algorithm based on the failure data to obtain failure causal relationships of the target detection algorithm under at least one failure type, the failure causal relationships comprising causal relationships between the plurality of failure influence factors; and iteratively calculating failure weight values of the plurality of failure influence factors based on the failure causal relationships corresponding to each failure type in the at least one failure type. The present disclosure can qualitatively and quantitatively analyze the failure causes of the target detection algorithm.

This disclosure provides a method, apparatus, and electronic device for attributing test anomalies, relating to the field of computer technology, particularly artificial intelligence, large-scale models, natural language processing, intelligent agents, deep learning, and mapping. The specific implementation scheme is as follows: Failed test cases corresponding to the failed target test task are clustered to obtain at least one cluster; for any cluster, multi-source operation and maintenance data related to the failed test cases within the cluster are acquired; using a large-scale model, based on the multi-source operation and maintenance data, failure attribution analysis is performed on the failed test cases within the cluster to obtain the failure cause analysis results of the target test task; wherein, the failure cause analysis results include the test failure root cause type and attribution description information.

This invention discloses an integrated method for early warning and quality improvement of critical elevator components under harsh environments. The method comprises the following steps: S1, collecting full lifecycle data of critical elevator components; S2, employing a composite analysis method combining Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FMA) to deeply explore failure causes and construct a quantitative failure mechanism model; S3, based on the failure mechanism model, selecting sensitive characteristic parameters and integrating machine learning algorithms to construct an adaptive early warning model, achieving real-time monitoring, risk classification, and accurate early warning of early component degradation under harsh environments; S4, based on the results of the failure mechanism model and the early warning model, formulating a multi-dimensional quality improvement plan. This invention can achieve accurate early warning of critical elevator components under harsh environments, prediction of remaining life, and tracing of quality problems, upgrading from "post-event maintenance" to "predictive maintenance + continuous quality improvement."

The application relates to the field of software testing and provides a failure case analysis method and device based on a large model driver, equipment and a storage medium. The method comprises the following steps: obtaining an execution result of a test case, screening out a test case with a failure state as a failure case based on the execution result of the test case, and obtaining an original data set associated with the failure case; the original data set is preprocessed to obtain a structured multi-modal feature representation; failure cause analysis is performed based on the multi-modal feature representation to generate an analysis report of the failure case; wherein the analysis report of the failure case at least comprises an analysis result of the failure case and a repair suggestion. The embodiments provided in the application can realize intelligent analysis and repair recommendation of software test failure cases in depth, self-adaptation and full-process coverage, and significantly improve the accuracy of problem positioning and the efficiency of test closed loop.

The present application belongs to the technical field of automated system testing, and discloses an automated penetration testing method, system, device and medium based on failure attribution driven task local re-planning, to solve the problems that the prior art cannot distinguish failure causes, lacks failure propagation blocking, and global re-planning destroys achievements. The method comprises: constructing a task graph, the task graph comprising nodes and edges representing dependency relationships; selecting a node satisfying a dependency condition to execute a test task; when the node fails to execute, generating a failure attribution code for distinguishing failure types; performing local pruning and path regeneration according to the attribution code: after pruning the failure branch, taking the execution results of the branch root node and the effective nodes as inputs, generating a new task node satisfying the connection constraint and connecting the task graph; performing consistency checking on the updated task graph, and continuing to execute the test. The method effectively improves the stability, traceability and execution efficiency of automated penetration testing.

The application provides a 5G message sending failure retransmission optimization method and system, comprising: constructing a multi-dimensional failure cause label for a message with sending failure, the label comprising a network state dimension, a user state dimension and a base station load dimension; calculating a dynamic weight based on the multi-dimensional failure cause label to obtain a failure feature vector for the message; adaptively selecting a retransmission strategy according to the failure feature vector, comprising delayed retransmission, fragmented retransmission, path switching retransmission and cross-protocol degradation retransmission; using a failure heat index (FHI) in the retransmission process, the index being calculated from failure frequency, failure concentration and network area failure density; performing priority scheduling on the message according to the failure heat index to determine whether to immediately retransmit or delay queuing; splitting the 5G message and using a progressive retransmission mechanism to preferentially transmit a message digest and key metadata, and then gradually supplementing the complete content after the network condition is improved.

The present disclosure provides a method, device, equipment and storage medium for automated testing, relating to the technical field of computer data processing, and particularly to the technical field of software testing. The specific implementation scheme is as follows: obtaining task information of a first test task that has been executed, the task information including task identification, task execution parameters and / or task execution results; obtaining case information of a test case executed by the first test task, the case information including program code and execution results of the test case; and displaying the task information and the case information. The method provides more basis for analyzing the failure cause of the test case by obtaining the task information and the case information of the executed test task, including the program code of the test case, and displaying the same, and is more conducive to quickly locating the failure cause of the test case.

The application discloses a transaction processing method and system, comprising: in response to a fund operation request from a business system, creating a flow record and marking the state of the flow record as processing; based on the fund operation request, initiating a fund operation instruction to a database; obtaining execution feedback of the fund operation instruction, if the execution feedback indicates that the operation is not successful, determining that the fund operation execution fails; determining the failure cause according to the execution feedback, and determining that the failure cause is retryable or non-retryable failure; if the failure cause is retryable transient failure, initiating a retry operation to the database; if the retry operation is still failed after N times of execution, the next step is executed; if the failure cause is non-retryable permanent failure, or the retry operation is still failed after N times of execution, a compensation transaction operation corresponding to the execution feedback of the fund operation is executed to realize rollback. Through the method, meaningless waiting and waste of retry resources can be avoided.

This invention relates to the field of operational status assessment technology, specifically to a method and system for assessing the operational safety status of electromechanical equipment. The method includes the following steps: constructing a probabilistic model of component state dwell time; comparing real-time data with anomaly benchmarks; generating a sequence of events to be executed; associating events with resource requirements and priorities; deducing state evolution under resource constraints; arbitrating resource competition events; updating the event sequence; and generating a safety status prediction result. In this invention, by constructing a probabilistic model of component state dwell time, equipment status assessment is transformed from static threshold judgment to dynamic probability prediction of future failure times. By introducing shared resource constraints and event priority mechanisms, resource conflicts are effectively arbitrated when simulating future state evolution, failures are predicted, and the accelerated deterioration path of failures caused by resource competition is revealed. This allows the prediction results to reflect the cascading impact of resource scheduling on the overall safety status of the equipment in real time, providing a basis for preventative maintenance decisions.

The present invention relates to a method and apparatus for automatic job recovery using resource requirement adjustment based on execution failure. The present invention comprises the steps of: detecting when an execution failure event caused by resource shortage occurs during job execution; quantitatively calculating the amount of resource shortage by analyzing the execution log of the job in response to the execution failure event; converting the calculated amount of resource shortage into a logical resource unit independent of the type of physical acceleration device or memory capacity; adjusting the resource requirement for the job based on the logical resource unit; generating a new execution specification corresponding to the job by reflecting the adjusted resource requirement; and re-executing the job according to the new execution specification. By utilizing execution failures caused by resource shortage occurring during job execution in a distributed computing environment not as simple errors but as meaningful feedback signals obtainable from the execution results, the resource requirement of the job can be automatically adjusted to recover the job.

Provided is a disk device capable of reducing the occurrence rate of failures caused by contaminants. According to an embodiment, the disk device is provided with: a disk that is rotatable; an actuator that supports and drives a head; a ramp that holds the head in an unloading position; a motor that rotates the disk; and a controller that performs a loading operation of loading the head from the ramp to the disk and a seek operation of moving the head from the outer periphery to the inner periphery side of the disk after the loading. If a radial movement speed of the head at the time of the loading operation is set to Vr1, a circumferential movement speed of the head is set to Vt1, a radial movement speed of the head at the time of the seek operation is set to Vrs, and a circumferential movement speed is set to Vts, the controller controls at least one of the radial movement speed of the head and the rotational speed of the disk in such a manner as to satisfy the relationship (Vr1 / Vt1)<(Vrs / Vts).