10results about How to "Reduce troubleshooting costs" patented technology

The invention discloses an automatic power distribution network fault processing method and system, and relates to the technical field of power distribution automation, and the method comprises the steps: monitoring power parameters in real time, recognizing abnormal nodes through an abnormality recognition method, and judging the types of the abnormal nodes; performing power test on the abnormal points, and generating corresponding processing signals for the faults based on test data; and a fault current signal is obtained and returned, and the fault point location is fed back to the online server processing platform. According to the method, the zero-sequence voltage transformer is used for monitoring the power parameters of the power distribution network in real time, so that faults in the power distribution network can be identified and diagnosed more accurately, and missing report and false report are reduced; by introducing an efficient positioning algorithm, the fault line and position can be quickly confirmed, the fault processing time is greatly shortened, and the risk of fault operation of the power distribution network system is reduced; and through rapid and accurate fault positioning, risks of equipment damage and fault expansion are reduced, and the overall operation reliability of the power distribution network is improved.

The invention relates to the technical field of coal mine bracket sensor detection, in particular to a sensor fault detection method based on bracket action change, which comprises the following steps: collecting data through a multi-source data fusion monitoring architecture, and filtering non-fault abnormal data through a Bayesian network; constructing a mapping model of bracket action and dip angle change, and defining monitoring logic; an inclination angle value is accurately predicted in combination with a Kalman filtering algorithm, the inclination angle value is compared with a real-time observation value, and intelligent early warning is triggered when an error exceeds a preset threshold value; the method realizes accurate and real-time fault detection, avoids missed judgment, greatly shortens the troubleshooting time, reduces the labor cost, improves the safety production guarantee capability of a working face, and is suitable for complex working conditions of an underground coal mine.

The utility model provides an intelligent wiring harness connector device of real time monitoring connection state relates to wiring harness connection technical field, including intermediate seat, the both ends fixed setting of intermediate seat have put pipe terminal, two groups put pipe terminal top fixed setting have the connection buckle, the inside below of intermediate seat is fixedly set with the battery, the shell of battery is wrapped insulating layer, and the electrode of battery sets in the top both sides of battery, the top of battery is fixedly set with the electromagnet A and electromagnet B, the middle fixed setting of connection buckle has the pilot lamp, the utility model discloses through the pilot lamp bright and dark state monitoring circuit, and the pilot lamp is always bright when the circuit is normal, and the abnormality flashes, and the circuit failure is visualized, compares traditional investigation mode, can fast identification failure, shortens the investigation time, reduces the failure processing cost of power system, communication base station and so on scene, has solved the problem that the existing connector is inconvenient real time monitoring.

The invention belongs to the field of FTU fault identification, and particularly relates to a zero sequence voltage abrupt change characteristic grounding fault identification method and system for FTUs, and the method comprises the steps: firstly obtaining a power distribution network bus zero sequence voltage discrete sequence, calculating a continuous sampling point difference absolute value sequence, and constructing an abrupt change characteristic quantity sequence through sliding time window integration; the time window features are input into a pre-trained capsule neural network, and the primary capsule layer encodes the time window features into primary capsule vectors; and updating the connection weight between the primary capsule and the high-level capsule to be converged through iterative computation, and judging a fault according to the converged high-level capsule vector: judging the fault and outputting a starting signal when the model length of the capsule in a grounding fault state exceeds a first threshold value and the included angle between the capsule and the fault feature base vector is smaller than a second threshold value, otherwise, judging that the system is normal. The FTU identification precision and efficiency can be improved.

The application provides a low-code application correctness verification method and system based on formal verification. The low-code application correctness verification method based on formal verification comprises the following steps: extracting a flow of a low-code application, setting a formal model for the flow of the low-code application; verifying the formal model, determining whether the low-code application meets performance requirements according to a verification result, and when the verification result indicates that the low-code application does not meet the performance requirements, performing a performance test on the low-code application to obtain test items that do not meet the performance requirements; extracting the test items that do not meet the performance requirements, and performing performance unqualified grade division on the test items to obtain unqualified grades. The system comprises modules corresponding to the method steps.

The application discloses a multi-data-source automatic acquisition and comparison system, relates to the technical field of data processing and automatic test, and comprises the following steps: a scheduling center generates a task context containing a task identifier and a time range; an intelligent sampler determines a sample set based on a strategy-driven random algorithm and a hash modulo operation; a data extraction agent acquires multi-source heterogeneous data in parallel through a unified interface and encapsulates the multi-source heterogeneous data into a virtual data context; a rule execution engine loads a declarative rule, converts data into standard semantic values, constructs a rule execution topology graph, traverses nodes according to a dependency relationship to execute logical assertions or blockages; and a report generation module outputs a diagnostic report by summarizing checking results and original data snapshots. Through full-link data tracking and topological rule checking, the application realizes accurate automatic comparison of multi-source heterogeneous data consistency in a distributed environment.

The invention discloses a lightweight method, system and instrument for fault monitoring and diagnosis of mechanical equipment parts, and belongs to the field of fault diagnosis. The model training module is used for constructing a lightweight convolutional neural network, comprises a feature extraction module, a feature enhancement module and a classification module, remarkably improves the running speed and memory efficiency while effectively extracting features, and inputs data samples into the network for training; the intelligent diagnosis module is used for judging the health state of the component according to the vibration signal and distinguishing normal and various fault types; a data and model storage module; and the man-machine interaction module integrates a Raspberry Pi, an analog-to-digital converter, a touch screen and other development portable instruments, and is used for issuing instructions to control the signal acquisition module, the model training module and the intelligent diagnosis module to execute corresponding functions and receiving feedback. According to the method, fault diagnosis of mechanical equipment parts can be accurately completed in real time, the parameter quantity and the calculated quantity are small, and efficient operation can be achieved.

The invention discloses a firmware upgrading method of a vehicle camera, a vehicle and a computer readable storage medium. The method comprises the steps of obtaining deserializer identification information of a vehicle domain controller in response to a firmware upgrading instruction; configuring a firmware upgrading communication link of the target camera to be upgraded according to the deserializer identification information; first identification information of the target camera is acquired through the firmware upgrading communication link; determining target upgrading firmware matched with the target camera from a firmware upgrading file package obtained in advance according to the first identification information; and according to the target upgrading firmware, performing firmware flashing on the target camera so as to perform firmware upgrading on the target camera. Therefore, the target upgrading firmware adaptive to the target camera can be automatically determined for firmware flashing, manual identification of camera models and matching firmware are not needed, the stability and reliability of vehicle camera firmware upgrading are guaranteed, the firmware upgrading efficiency is improved, and then the batch upgrading requirement of cameras is met.

The invention relates to a fault processing method and device, computer equipment, a storage medium and a computer program product, and the method comprises the steps: determining a communication state between a target node and a fault node under the condition that any other node in a distributed cluster is detected to have a fault, and carrying out fault processing on the target node and the fault node under the condition that the communication state is normal. And determining the working state of the system disk on the fault node, and processing the fault according to the working state. According to the method, when the distributed cluster breaks down, the fault node can be recovered to be normally used as much as possible by analyzing the working state of the fault node and remedying the fault, and compared with a method of directly isolating the fault node, the fault remedying method can save the resource cost of the node. Moreover, for the fault nodes which are restored to be normal after being remedied, manpower resources required by manual troubleshooting can be reduced, and thus the fault processing cost is further reduced.

The present application relates to a vehicle fault diagnosis method, device, computer equipment, storage medium and computer program product. The method comprises: obtaining a vehicle fault description text; inputting the vehicle fault description text into a pre-trained cross-expert fine-grained shared neural network model, and outputting a vehicle fault diagnosis result; wherein the cross-expert fine-grained shared neural network model comprises a first input processing module, an encoder module, a second input processing module, a decoder module and an output module. The use of this method can improve the efficiency of vehicle fault diagnosis and reduce the cost of vehicle fault diagnosis.