47 results about "Failure type" patented technology

The invention provides a single-mode semiconductor laser failure assessment method and device and electronic equipment, and relates to the technical field of semiconductor lasers. The method comprises the following steps: acquiring an optical power current characteristic test curve of the failed single-mode semiconductor laser; determining an optical power parameter change trend of the failed single-mode semiconductor laser according to a preset optical power current characteristic standard curve and the optical power current characteristic test curve of the failed single-mode semiconductor laser; if the optical power parameter variation trend indicates that the optical power parameter in the working current range is zero, determining that the failure type is optical cavity surface catastrophe damage; and if the optical power parameter variation trend indicates that the optical power parameter in the working current range is non-linearly decreased, determining that the failure type is optical body material catastrophe damage, so that the failure type of the single-mode semiconductor laser can be judged through the optical power current characteristic test curve. The evaluation cost can be reduced, the evaluation efficiency is improved, and the accuracy of evaluation results is ensured.

The invention relates to the technical field of mechanical failure analysis, in particular to a failure detection method for a planetary gear shaft of a differential mechanism of a new energy automobile, which comprises the following steps of: processing and analyzing vibration, load and temperature signals by simulating working conditions, and extracting and analyzing typical characteristics of the signals; the aging state and the specific failure type of the planetary gear shaft can be quickly judged, the failure detection period can be effectively shortened, and the detection cost can be reduced.

The invention discloses a vehicle failure control method and device, and belongs to the technical field of automobiles. Acquiring vehicle data in the driving process; failure types and associated failure conditions are obtained, the failure types comprise at least one of sensing system failure, control system failure, execution system failure and front hatch cover system failure, the sensing system failure comprises visual sensing failure and radar sensing failure, and the control system failure comprises decision planning failure and control execution failure; the execution system failure comprises brake system failure, steering system failure and power system failure, and the front hatch cover system failure comprises front hatch cover locking failure; determining a target failure type according to a failure condition met by the vehicle data; and determining a control mode according to the target failure type. According to the method, the failure condition is preset for each failure type, matching and judgment are performed according to the vehicle data, the fault source is accurately positioned, the appropriate control mode is dynamically determined according to different target failure types, and differentiated intelligent decisions are realized.

The invention discloses a configuration item repair-oriented Linux kernel boot test and configuration consistency intelligent repair method. The method comprises the following steps: collecting a compiling log, a boot log and a running state; performing segmentation, pattern matching and field extraction on the log, normalizing error information into structured error entries, and distinguishing a dependency missing class, a mutual exclusion conflict class and other compiling failure classes; performing preprocessing and grammar analysis on the Kconfig file, extracting configuration item attributes and constraint relationships, constructing a unified dependency graph and generating a configuration item metadata table; classifying and marking the configuration items based on the metadata, and calculating a key score; generating a repair action according to the structured error entry, the unified dependency graph and the key score, performing iteration execution, and performing recompilation and restart verification after each iteration until a reparated configuration file which can be compiled and started is obtained or a preset iteration upper limit is reached; according to the invention, the cross-version adaptation and problem closed-loop efficiency is improved.

The present disclosure relates to methods and apparatuses. According to some embodiments of the disclosure, a method includes: transmitting, at an access node, a failure information message to a base station, wherein the access node is connected to the base station via a master node and a secondary node, and wherein the failure information message includes a failure type of a backhaul link. The failure type is based on a failure type set including at least the following: an expiry of a physical layer problem timer, a random access problem, and reaching a maximum number of retransmission in response to radio link failure (RLF).

A processing method includes determining a first procedure phase in which a failure occurs in an over-the-air (OTA) technology update process; determining a first failure type from a plurality of OTA failure types based on the first procedure phase; and determining at least one processing strategy based on the first failure type.

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.

Described are systems and methods for memory failure analysis based on bitline threshold voltage distributions. An example method of implementing a failure type prediction model includes: receiving, by a processing device, a first failure-related dataset reflecting a first bitline threshold voltage distribution associated with a first memory device; determining, based on the first failure-related dataset, a first failure type distribution for the first memory device; creating a training dataset comprising the first failure-related dataset and the first failure type distribution; and training, using the training dataset, a failure type prediction model to determine, for a second memory device, a second failure type distribution based on a second failure-related dataset comprising second bitline threshold voltage data associated with a second memory device.

The invention provides a cradle head failure inspection and position covering method based on neighbor machine cooperation and related equipment, and relates to the technical field of general control or adjustment systems. The method comprises the following steps: firstly, determining a digital twinborn model of each holder in a target area so as to judge whether a target holder fails or not; the method comprises the following steps: determining a gap region corresponding to a failure holder preset position by combining a digital twin model and a failure type, dividing the gap region into a plurality of gap sub-regions with gap risk value identifiers, matching candidate neighbor units for each gap sub-region, and determining an optimal takeover holder; and generating a routing inspection execution task sequence containing position complementing newly-added tasks in combination with the digital twin model, controlling the optimal takeover holder to complement positions according to the sequence, triggering a takeover exit process after the failed holder returns to normal, and recovering the original routing inspection plan of the optimal takeover holder. By implementing the method, the optimal resource can be dynamically and intelligently scheduled to make up the monitoring blind area, and the robustness and reliability of the whole monitoring system are improved while the interference to the normal inspection task is minimized.

The present disclosure provides a fan control method, a fan control device, an electronic device and a storage medium. The method comprises: determining a plurality of first target fans that have failed according to the operating parameters of each fan in a multi-fan device; determining the failure type corresponding to each first target fan according to the failure duration and / or the cumulative number of failures of each first target fan; determining the recovery strategy corresponding to each first target fan according to the failure type corresponding to each first target fan; and executing the recovery strategy corresponding to each first target fan according to the recovery priority of each first target fan; wherein the recovery priority represents the degree of influence of each fan on the operation of the multi-fan device. The present disclosure can realize differentiated recovery processing of multiple failed fans, effectively adapt to the actual needs of different failure scenarios, and improve the overall operation reliability and failure recovery efficiency of the device.

This application discloses a method and apparatus for quantitatively evaluating the failure risk of oil and gas gathering and transportation pipelines, relating to the field of data processing. The method includes: dividing the pipeline under test into multiple first pipe segments; classifying multiple typical failure types into internal corrosion failure and other failures; for each first pipe segment, determining the basic risk values ​​for internal corrosion failure and other failures based on a preset risk scoring table, obtaining a first basic risk value and a second basic risk value; correcting the first basic risk value based on a risk correction factor, obtaining a corrected risk value; the risk correction factor is dynamically determined based on the risk of slug flow occurring in the pipe segment; determining the comprehensive risk value of the pipe segment based on the corrected risk value and the second basic risk value; and obtaining the failure risk assessment result of the pipeline under test based on the comprehensive risk value of each first pipe segment. The scheme provided by this application can improve the accuracy and reliability of the assessment results and enhance the generalization ability of the assessment system.

Provided in embodiments of the present disclosure are a method and apparatus for determining a DAPS handover failure type. The method includes receiving DAPS handover failure related information sent by a terminal; and determining a DAPS handover failure type on the basis of the DAPS handover failure related information. In the embodiments of the present disclosure, a DAPS handover failure type is determined on the basis of DAPS handover failure related information reported by a terminal, which is suitable for a situation in which both a source cell side connection and a target cell side connection may fail in a DAPS handover process, implementing mobility robustness optimization for a DAPS handover process, assisting network optimization, and improving the handover success rate.

The invention provides a fault protection device for a reduction gearbox of an electric automobile, and belongs to the field of automobiles, the fault protection device comprises a monitoring unit and a strategy unit, the monitoring unit is used for obtaining state data of a gear shifting motor, the reduction gearbox and an electric drive assembly controller and judging whether a fault occurs or not; the strategy unit outputs control strategies of the gear shifting motor and the reduction gearbox to the electric drive assembly controller according to fault information of the monitoring unit and fault types, according to different faults of the gear shifting motor and the reduction gearbox in the reduction gearbox and different protection actions on the reduction gearbox, power is not cut off blindly, speed limiting is not conducted blindly, and the influence on drivability is reduced.

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.

The invention discloses a multi-channel non-Lambert surface FPP-PS fusion depth measurement method guided by a failure typing module, and aims to solve the problems that a failure region under a single failure type is generally judged on the basis of a fixed threshold value or an empirical rule in the conventional method, a real mixed failure form is difficult to accurately establish, and the effectiveness of a subsequent repair or fusion algorithm is limited. According to the invention, after the obtained multi-source physical features are used to construct a multi-source joint feature model, the multi-source joint feature model is processed by a failure feature encoder to respectively form prototype vectors; multi-source joint feature data of an actual to-be-detected object and each prototype vector are substituted into the failure weight calculation model to obtain weights of corresponding repair channels, and finally a calculation result of a repair phase is output for each repair channel in a weighted fusion mode; the overall process of the method comprises four stages of multi-source physical feature construction, failure feature coding and prototype modeling, failure weight calculation and multi-channel phase repair fusion, and all the stages are cooperatively completed in the same calculation framework.

The application provides a chip failure type test system, comprising: a detection device configured to output a detection signal to a chip under test; a processing device in communication with the detection device and in electrical communication with the chip under test, the processing device configured to control at least a test frequency and a test power of the detection signal to test the chip under test, and determine whether the chip under test fails and determine a failure type in the case of failure according to an output signal of the chip under test during the test. The system connects the processing device with the detection device, controls at least the test frequency and the test power of the detection signal by the processing device to test the chip under test, and determines whether the chip under test fails and determines a failure type in the case of failure according to an output signal of the chip under test during the test, realizes automatic control of chip testing, and further solves the problem that chip failure types cannot be automatically identified and distinguished in the prior art.

The invention relates to a method and a device for determining a failure type of a storage unit and computer equipment. The method comprises the following steps: acquiring test data corresponding to a failure storage unit in a memory; wherein the test data comprises a logic address of the invalid storage unit in the memory; mapping the logic address into a physical address of the invalid storage unit in the memory layout; according to a preset layout structure rule and a physical address, classifying the failure storage units, and determining a failure type; wherein the layout structure rule comprises an incidence relation between a common physical structure and a physical address of the memory layout. By adopting the method, failure classification errors of the storage units can be reduced, and the accuracy of failure classification is improved.

The application provides a gimbal failure inspection and replacement method based on neighboring machine cooperation and related equipment, and relates to the technical field of general control or regulation systems. The method comprises the following steps: first, determining the digital twin model of each gimbal in the target area to determine whether the target gimbal is failed; combining the digital twin model and the failure type to determine the gap area corresponding to the preset position of the failed gimbal, and dividing the gap area into multiple gap sub-areas with gap risk value labels; then, matching candidate neighboring machines for each gap sub-area and determining the optimal takeover gimbal; combining the digital twin model to generate an inspection execution task sequence containing a replacement task; controlling the optimal takeover gimbal to replace according to the sequence; after the failed gimbal returns to normal, triggering the takeover exit process and restoring the original inspection plan of the optimal takeover gimbal. The method can dynamically and intelligently schedule optimal resources to compensate for the monitoring blind area, minimize the interference with normal inspection tasks, and improve the robustness and reliability of the entire monitoring system.

This invention discloses a data classification method, apparatus, storage medium, and terminal for chip failure types. The method includes acquiring design information of the chip under test (DUT), and based on the design information, acquiring the DUT's size information, logical location-to-physical location correspondence information, and failure type information; acquiring test data of the DUT, and converting the test data into test physical location information based on the size information and logical location-to-physical location correspondence information; clustering the failure positions of the DUT using an optimized clustering algorithm based on the test physical location information to obtain failure clustering results; and classifying the failure positions of the DUT based on the failure type information and the failure clustering results to obtain failure classification results for the DUT. This method improves the efficiency of clustering operations by optimizing the clustering algorithm, saves memory occupied by the operation, and greatly improves the efficiency of chip failure type acquisition; it also improves data reading and saving efficiency and saves storage space.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a transfer transaction failure compensation method and device, and the method comprises the steps: responding to a transfer transaction failure record, obtaining a transaction log, extracting a transaction feature vector according to the transaction log, inputting the transaction feature vector into a first classification model, and obtaining a first transaction failure type; if the first transaction failure type belongs to the preset classification set, processing according to a preset compensation mechanism; if not, using the word vector model to perform word meaning feature vector extraction and inputting the word meaning feature vector into a second classification model to obtain a second transaction failure type; if the second transaction failure type belongs to the preset classification set, processing according to a preset compensation mechanism; and if not, generating error information and sending the error information to operation and maintenance personnel. Through the embodiment of the invention, the transaction failure type identification accuracy can be improved, so that the transfer failure processing efficiency is improved, and the manual intervention cost is reduced.

There may provide a device and method for controlling an inverter, and a steering system using the same. The device for controlling the inverter may include a failure determiner configured to determine, in response to an occurrence of a failure in an inverter circuit connected to a motor, whether the failure corresponds to a predetermined failure type based on a braking torque of the motor, and a controller configured to control, if the failure corresponds to the failure type, a switching operation of the inverter circuit so as to reduce the braking torque of the motor based on the failure type.

The invention discloses an automatic diagnosis method and device for a fault type of a transmission chain bearing of a wind generating set. The method comprises the following steps: acquiring related file data; performing multi-angle preprocessing to obtain n pieces of CMS reconstruction vibration data; extracting a multi-dimensional characteristic value, and determining a corresponding fault level after normalization weighting; m optimal resonance bands of each piece of CMS reconstruction vibration data are determined; determining n vibration characteristic indexes and m fault diagnosis spectrum sets, wherein each fault diagnosis spectrum set comprises n fault diagnosis spectrums; for each optimal resonance band, n1 effective maps are screened from the corresponding n fault diagnosis spectrums, the occurrence probability of each fault type in each effective map is determined, and the bearing fault type with the maximum occurrence probability is taken as the corresponding bearing fault type; and combining the m bearing fault types, the corresponding final fault maps and the fault levels to form a fault type diagnosis result, and outputting the fault type diagnosis result. According to the invention, the fault type and the fault degree can be accurately diagnosed, and meanwhile, the diagnosis precision is improved.

The present disclosure provides a distributed job system control method, device, equipment, medium and program product, relates to the technical field of computer application, and particularly relates to the technical field of distributed job. The specific implementation scheme is: for a first container carrying a first process, in response to detecting that the first process is triggered to terminate based on the first container failing, determining a current failure type of the first container; if the current failure type is consistent with a target failure type, rebuilding the first container, and restarting the first process based on the rebuilt first container. The present disclosure can rebuild the container for the failure type that can successfully rebuild the container, and does not rebuild the container for the failure type that cannot successfully rebuild the container, so as to save the system operation cost and meet the job demand.

The invention discloses a multi-modal data generation method and system for failure event injection in a complex scene, and belongs to the technical field of computer data generation. The method comprises the following steps of: 1, constructing a failure action and a recovery action, including obtaining and expanding an original script and defining and realizing a regularized failure type; 2, generating multi-modal data based on the failure action and the recovery action constructed in the step 1; and step 3, based on the multi-modal data generated in the step 2, forming a data set which can be directly used for VLMs failure perception evaluation through quality control, template design, truth value labeling and data division. The method is used for constructing a multi-modal reasoning data set, the data set comprises various failure events and corresponding recovery actions, and evaluation data in a video question-answer pair form is constructed based on multi-modal data and annotation files.

Provided in the present disclosure are an information processing method and apparatus for a conditional handover, and a terminal and a device. The method comprises: sending failure information of a conditional handover candidate secondary cell group to a network side device, wherein the failure information is used for determining a failure type of the conditional handover candidate secondary cell group.

Embodiments of this application provide a processing method and apparatus, and a carrier. The method includes: determining a first procedure phase in which a failure occurs in an over-the-air OTA technology update process; determining a first failure type from a plurality of OTA failure types based on the first procedure phase; and determining at least one processing strategy based on the first failure type. According to the foregoing method, in the OTA update process, a cause of an OTA update failure can be accurately evaluated, and impact of the OTA update failure can be evaluated, to provide a corresponding processing strategy and suggestion.

To provide a test support device capable of designing failures that match the state of microservices, which may constantly change.SOLUTION: A processor of a test support device 100 comprises: a test execution unit 340 that, on the basis of microservice information 200, selects microservices and reliability functions configured in a system to be tested and serving as targets for failure testing, and that, on the basis of failure conditions 220, selects a failure type of a failure to be caused in the microservices during a test; and a failure setting information generation unit 330 that, on the basis of microservice state information 210, selects a microservice in which a failure is to be caused, determines setting values of failure-setting items for the microservice and the failure type, and generates failure setting information 230.SELECTED DRAWING: Figure 1

Described are systems and methods for memory failure analysis based on bitline threshold voltage distributions. An example method of implementing a failure type prediction model includes: receiving, by a processing device, a first failure-related dataset reflecting a first bitline threshold voltage distribution associated with a first memory device; determining, based on the first failure-related dataset, a first failure type distribution for the first memory device; creating a training dataset comprising the first failure-related dataset and the first failure type distribution; and training, using the training dataset, a failure type prediction model to determine, for a second memory device, a second failure type distribution based on a second failure-related dataset comprising second bitline threshold voltage data associated with a second memory device.