494 results about "Fault occurrence" patented technology

The invention relates to an electromechanical fault prediction and diagnosis method and system based on big data, and the method comprises the steps: collecting the operation state data of electromechanical equipment in real time, and synchronously obtaining historical associated data; performing dynamic feature extraction on the operation state data and the historical associated data, constructing a sliding mean value feature matrix, and calculating dynamic weights of feature parameters; generating a fusion weight coefficient according to the dynamic weight and a preset fault threshold interval; extracting a distribution density curve of a historical fault occurrence probability, and calculating a dynamic threshold value; performing weighted reconstruction on the sliding mean feature matrix based on the fusion weight coefficient, and outputting a fault type and an occurrence probability through a pre-trained lightweight residual neural network model; and when the fault occurrence probability exceeds a dynamic threshold value adjusted based on a historical fault occurrence probability distribution density curve, generating an electromechanical fault diagnosis result so as to realize the purposes of real-time monitoring of the operation state of the electromechanical equipment and accurate fault prediction and diagnosis.

The invention relates to the technical field of power generation equipment control, and discloses an intelligent control method and system for a hydroelectric generating set speed regulator, and the method comprises the steps: employing an introduction variable structure and variable parameter control technology, and dynamically adjusting the control parameters of a power closed loop and an opening closed loop; real-time calculation and feedback control are carried out, and a speed regulator responds to a power grid frequency modulation Operating data of key parts of the speed regulator are collected in real time through a sensor, feature extraction and mode recognition are conducted on the data in combination with an artificial intelligence algorithm, and the operating state and potential faults of the speed regulator are recognized; constructing a fault tree model based on the speed regulator state monitoring parameters, carrying out hierarchical analysis on the speed regulator, and positioning the specific position and reason of the fault; and verifying and optimizing the fault diagnosis result, and generating a targeted fault processing suggestion. The system comprises a dynamic adjustment module, a state evaluation module and a fault analysis module. According to the invention, the operation efficiency and safety of the hydroelectric generating set speed regulator in a complex power grid environment are obviously improved.

The invention discloses a power transmission line abnormity early warning and fault positioning system, which belongs to the technical field of line detection, and specifically comprises the following steps: dividing early warning equipment in a line into sub-regions according to distribution through a region delimiting module; when a fault occurs, the region positioning module quickly marks the related sub-regions and equipment as undetermined states; the data acquisition module is used for collecting data of the to-be-determined equipment in a set time window before a fault, and preprocessing the data to judge whether the fault is caused by an electrical problem or not; if yes, collecting historical power consumption data of the to-be-determined area; the fault positioning module normalizes historical data, constructs a time sequence model, extracts a current value change sequence, divides the current value change sequence into a plurality of subsequences and then performs clustering analysis; judging the fault reason according to the offset condition of the category cluster where the undetermined current value change sequence is located in the clustering result; if the offset exceeds a preset threshold value, considering that the electric equipment is abnormal; otherwise, the aging of the line insulating material is caused.

A method, system, and readable storage medium for fault line selection in distribution networks is provided. The method includes: obtaining the zero-sequence current of each feeder and the zero-sequence voltage of the busbar within a preset time window after a fault occurs; using these to process the feeder's short-time window zero-sequence instantaneous power curve cluster in the distribution network through KPCA (Kernel Principal Component Analysis) for dimensionality reduction, determining the principal component scores; and performing BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering based on these scores to identify whether a feeder is faulted. This clustering process allows for precise and rapid identification of the faulted feeder, even when the current is small, improving detection accuracy. This solves the problem of quickly identifying the faulted feeder in a small current grounding distribution network during single-phase grounding faults.

The invention provides a circuit board electrical performance test and diagnosis method, which comprises the following steps of: constructing a structured process-electrical data set by collecting process parameters such as etching time, pressing temperature, drilling precision, copper foil thickness and the like and electrical test indexes such as impedance deviation, leakage current, signal attenuation and the like; screening key process variables by adopting mutual information analysis and a maximum correlation-minimum redundancy criterion; through modeling of a three-layer causal diagram and a structural equation, quantitative influence evaluation of process parameters on electrical performance and fault types is realized; when a fault is detected, the system can automatically perform reverse reasoning, identify a main manufacturing deviation item and generate attribution diagnosis and process optimization suggestions, so that the accuracy of fault diagnosis and the pertinence of process adjustment are improved, and the fault occurrence rate is reduced and the product quality is improved.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

The invention discloses an intelligent control method and system based on the operation state of power equipment, and relates to the technical field of power equipment safety, and the control method comprises the steps of data collection, feature extraction, model establishment, intelligent control, model optimization and maintenance and storage. The method has the advantages that the power equipment is divided into high, medium and low risk levels, and the data acquisition frequency is dynamically adjusted according to the fault probability and the deviation degree of the key operation parameters, so that targeted data monitoring can be realized, the data acquisition efficiency is improved, the equipment state change can be responded in time, and the reliability of the power equipment is improved. The method effectively reduces the risk of fault occurrence, analyzes the feature data through employing the K-means clustering algorithm, can accurately divide the data into a normal data set, an abnormal data set and a fault data set, improves the intelligent degree of data processing, and also provides a high-quality data basis for subsequent model training.

The invention discloses a protection measurement and control device power supply fault early warning method and system and a storage medium. The invention aims to solve the problem that fault early warning and detection cannot be accurately carried out when power supply fault early warning is carried out by monitoring ripple change in the prior art. The method comprises the following steps: establishing a transition mapping relation from a normal state to a fault state according to physical parameter characteristic changes in a historical fault scene; respectively acquiring each physical parameter characteristic according to a set sampling frequency, and respectively adjusting each sampling frequency according to the sampled physical parameter characteristics; the sampled physical parameter characteristics are imported into a power supply fault early warning model, and a fault scene of the power supply of the measurement and control device is matched; and executing preset processing preparation according to the matched and predicted fault scene. And a power supply fault early warning and evaluation model is established according to the transition mapping relationship, so that the fault occurrence precursor can be found as early as possible, and the power supply fault early warning and detection cannot be accurately carried out.

The invention discloses a non-stationary industrial process early fault diagnosis method based on a common trend model, and belongs to the field of industrial process monitoring and fault diagnosis. The method comprises the following steps: collecting training data of a non-stationary industrial process under a normal working condition, performing denoising by using singular spectrum analysis, and obtaining a stationary projection matrix and a stationary data matrix by using a common trend model; orthogonal conversion is carried out on the stationary data matrix, a conversion projection matrix is calculated, and an orthogonal conversion data matrix and a statistic data matrix thereof are obtained; calculating a mean value and a covariance matrix of the statistic data matrix to obtain a control limit; the method comprises the following steps: acquiring test data under a real-time working condition of an industrial process, processing to obtain statistic data, calculating a mahalanobis distance index, and comparing with a control limit to judge whether a fault occurs; and if a fault is detected, realizing fault separation according to fault reconstruction and a mapping relation. According to the method, the interference of the non-stationary characteristic on the fault diagnosis model can be reduced, and fault detection and separation in the non-stationary process are realized.

The invention relates to the technical field of power system automation, and particularly discloses an intelligent fault diagnosis and analysis system and method for power secondary equipment, and the method comprises the following steps: constructing a sensor network, collecting the operation data of a power system, and extracting the characteristics related to the fault of the power secondary equipment in the operation data; constructing a fault diagnosis model of the power secondary equipment, and obtaining faults of the power secondary equipment in the monitoring time period; constructing an association period according to the fault occurrence time point, generating coordinate points according to the fault occurrence sequence in the association period, and clustering the coordinate points to obtain a cluster; and according to the density of the clustering clusters, strong correlation faults of the faults are obtained, and when the faults occur, prompt information is sent to maintain the strong correlation faults corresponding to the occurring faults in advance. According to the invention, the real-time accurate diagnosis, strong correlation early warning and interpretable visual root cause analysis of the power secondary equipment fault are realized, and the safe operation of the system is effectively guaranteed.

The invention discloses a fault prediction and early warning system and method based on multi-sensor monitoring, and belongs to the field of big data, and the method comprises the steps: deploying various types of sensors, collecting equipment operation data in real time, and forming a multi-dimensional original data set; preprocessing the data to remove abnormal values and missing values, removing noise in the sensor data by adopting a filter, converting the data of different dimensions into a unified range, and extracting time domain features and frequency domain features from the preprocessed data; fusing the features of different sensors to form a comprehensive feature vector; training a fault prediction model by using a machine learning algorithm based on the historical data; and inputting sensor data collected in real time into the trained model, calculating a fault occurrence probability, setting an early warning threshold value, and triggering early warning when the predicted fault probability exceeds the threshold value. The device operation data is collected in real time, potential faults are found in time, early warning is carried out in advance, the device shutdown time is shortened, and the production efficiency is improved.

The invention discloses an information operation and maintenance monitoring system, which belongs to the technical field of information operation and maintenance monitoring and comprises a heterogeneous data acquisition layer, a streaming processing engine, an intelligent analysis center, a closed-loop control module, a visual platform and a safety protection system. Physical equipment, virtualized resources and a cloud native environment can be fully covered, the breadth and depth of data acquisition are ensured, the adaptive sampling and edge computing technology improves the efficiency and real-time performance of data acquisition, the data transmission delay and the central processing pressure are reduced, a streaming processing engine and an intelligent analysis center are combined with various algorithms and models, and the real-time performance of data acquisition is improved. Millisecond-level data processing and anomaly detection are realized, the accuracy and timeliness of fault identification are improved, the root cause positioning and fault prediction functions help operation and maintenance personnel to quickly position the root cause of a problem and formulate preventive measures, and the fault occurrence probability and the influence range are reduced.

The invention discloses a big-data-oriented real-time analysis and fault positioning system, and the system comprises a data collection module which is used for collecting a multi-source data stream, and constructing a high-dimensional feature data set; the confrontation feature compression module is used for compressing high-dimensional features through multi-layer nested mapping and generating initial potential feature representation; the sparrow population module is used for constructing a sparrow individual population containing a plurality of hyper-parameter combinations; the feature evolution analysis module is used for deploying the optimized analysis confrontation model to a big data platform; the abnormal event detection module is used for carrying out sliding window analysis based on the change amplitude and the deviation trend of the potential characteristic track in the time sequence and judging whether a real-time abnormal event is formed or not; the historical alignment matching module is used for marking a specific time index and a source channel of fault occurrence; and the label identification module is used for completing fault type identification and fault type label output based on the fault occurrence time index and the source channel. According to the invention, an accurate big data real-time analysis and fault positioning scheme is provided for the user.

The invention discloses a power distribution system fault analysis method and system. The method comprises the steps of collecting operation related data of a power distribution system in real time through various sensors and intelligent metering equipment; preprocessing the collected operation related data to remove noise and interference signals, and establishing a multi-dimensional feature set of the real-time operation state of the power distribution system; extracting potential fault features from the multi-dimensional feature set by using a convolutional neural network, and generating a fault feature vector; performing time sequence analysis on the fault feature vector by using a recurrent neural network, and identifying a possible fault type in the current power distribution system; and in combination with the topological structure and the electrical model of the power distribution system, spatial positioning is performed on the identified fault type based on the graph neural network, and specific nodes of the fault and corresponding fault severity levels are output. According to the embodiment of the invention, the fault detection capability and response speed of the power distribution system can be remarkably improved, and the power distribution system is promoted to develop towards intelligentization and automation directions.

The invention discloses a power distribution network traveling wave switch fault positioning precision verification method and system, and relates to the technical field of power distribution networks, and the method comprises the steps: simulating an actual cable line, checking the length of the line, and obtaining a power supply needed by a power distribution network traveling wave switch in combination with the voltage of a transformer isolation line; the fault occurrence time of the resistor action simulation line is controlled, different user loads of the line are simulated in combination with the capacitor charging and discharging action, and line electrification or fault occurrence and power failure are judged through a line electrification indication signal; and configuring the traveling wave switch based on a power supply required by the power distribution network traveling wave switch, and verifying the fault positioning precision of the power distribution network traveling wave switch in combination with the line length, the line fault occurrence time and the fault simulation result. Traveling wave monitoring points are reasonably arranged according to the length of a line, it is ensured that traveling wave signals can be rapidly monitored when a fault occurs, and the traveling wave signals when the fault occurs and the time difference of the signals transmitted to the monitoring points are collected through the configured traveling wave switch.

The invention discloses a power grid fault diagnosis system and method based on big data, belongs to the technical field of fault diagnosis, and solves the technical problems that in the prior art, only a line section where a fault occurs can be diagnosed, the type of the fault cannot be judged, and subsequent fault processing is not facilitated. The method comprises the following steps: constructing a fault anomaly library through historical data, and analyzing real-time data based on the fault anomaly library to obtain a preliminary diagnosis result; technicians can prepare in advance; integrating the real-time data into an index feature sequence, calling a fault diagnosis model, and inputting the index feature sequence into the fault diagnosis model to obtain a fault tag; matching a corresponding fault type based on the fault tag; the accuracy and the reliability of diagnosis can be improved; fault position information is obtained according to the data sensor and fault type analysis, and a technician closest to the fault position is dispatched to process the fault; and the fault can be timely processed, and greater influence caused by untimely processing can be avoided.

The invention relates to the technical field of cable fault testing, in particular to a multi-core cable fault testing system and method, and the method comprises the steps: determining a fault generation coefficient of a target core wire testing current signal based on the difference between periods where all adjacent signal wave crests in the target core wire testing current signal are located and a periodic parameter; determining the composition richness of the target core wire test current signal based on the similarity of related data sets between the target core wire and all other core wires under different voltage conditions and the signal amplitude distribution near any maximum value point in the target core wire test current signal; and performing forward fusion on the composition richness and the fault occurrence coefficient, adjusting a preset initial decomposition interval scale, screening part of component signals for reconstruction, and performing fault detection on the reconstructed signals. According to the invention, accurate analysis of the test current signal is realized, and the accuracy of fault diagnosis is improved.

The invention provides a fault diagnosis method, a fault knowledge graph construction method and device and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring fault alarm data, wherein the fault alarm data refers to data describing fault occurrence conditions; identifying fault entity information in the fault alarm data, wherein the fault entity information comprises at least one of fault entities, relationships among the fault entities and attributes of the fault entities; and based on the fault entity information, searching is carried out in the fault knowledge graph to obtain a fault diagnosis result, and the fault diagnosis result is used for describing a fault reason corresponding to the fault alarm data and / or a fault processing measure.

The invention discloses an electrical pipeline fault early warning method and system, and relates to the technical field of electrical pipelines, and the method comprises the steps: collecting electrical pipeline data, carrying out the preprocessing of the collected data, packaging the processed data, transmitting the packaged data to a central processing unit, analyzing the data, extracting characteristic parameters, and combining a predefined rule base, the method comprises the following steps: carrying out fault feature identification and abnormity determination, matching a fault mode based on identification features, evaluating a fault type and a severity degree, predicting a fault probability in combination with a historical data trend, automatically generating an early warning message when real-time data exceeds a preset threshold value, sending structured early warning information through a multi-mode communication channel, and tracking and confirming a state. And triggering preset emergency operation, and meanwhile, providing a manual intervention interface and recording a processing process. By monitoring the parameters of the electrical pipeline in real time and triggering an early warning mechanism, the safety problem is identified and processed before the fault occurs, the downtime is reduced, the reliability of the pipeline is improved, and preventive maintenance is realized so as to reduce the emergency maintenance cost.

The invention, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

The invention discloses an error correction method of a neural network processor and a related device, and belongs to the technical field of neural networks. The method comprises the steps that according to a fault PE table obtained through error detection, the fault PE table comprises a two-dimensional list of fault occurrence and position information, a newly detected fault PE is positioned according to the fault PE table, and a multi-stage fault-tolerant decision is executed: i, a fine-grained error correction stage; ii, a coarse granularity error correction stage; according to the current error correction mode, a pipeline submission mechanism or a submission mechanism is selected, and a calculation result is output. According to the method, a mixed granularity fault recovery scheme is adopted, and the error correction function can be achieved under the condition that any standby PE is not added. The fine-grained scheme provides more flexibility and does not waste PE, and the coarse-grained scheme ensures that the reasoning precision is not reduced along with the increase of the failure rate.

The embodiment of the invention provides a fault processing system and method, equipment and a medium, and relates to the field of system fault problem analysis. The system comprises a data mining processing unit and a monitoring unit, the monitoring unit is used for monitoring environment information of the server; and the data mining processing unit determines the fault occurrence time of the external device with the fault, obtains the environment information of the server through the monitoring unit, screens out the environment information of the server in a target time period corresponding to the fault occurrence time, and records and stores the environment information. Through the mode, fault analysis personnel can comprehensively analyze the information of the fault occurrence moment, accurately obtain the working state of the server within a period of time before and after the fault mode moment, analyze the fault problem more conveniently and clearly, and record the information of the fault moment, so that the working state of the server can be accurately analyzed. The method can avoid the situation that the fault phenomenon cannot be reproduced due to the timeliness problem, and the judgment of the fault reason is affected, and greatly improves the fault problem processing efficiency.

The invention discloses an intelligent operation and maintenance method, system and equipment based on deep learning and a medium, and the method comprises the steps: obtaining historical operation and maintenance data, predicting the historical operation and maintenance data, inputting the predicted historical operation and maintenance data into a preset fault prediction model based on deep learning for training, and bringing the predicted historical operation and maintenance data to the trained fault prediction model; acquiring real-time operation and maintenance data, processing the real-time operation and maintenance data, inputting the processed real-time operation and maintenance data into the trained fault prediction model, and predicting to obtain a fault occurrence probability; and if the fault occurrence probability is greater than a preset fault threshold value, triggering automatic operation and maintenance repair processing. Compared with the prior art, the method and the device have the advantages that real-time analysis and prediction are performed on the real-time operation data of the system by using the prediction model of deep learning, so that possible faults can be predicted and early warned in advance, an automatic repair function is introduced, and when potential risks are detected, the problem can be autonomously tried to be repaired.

The invention discloses a network operation and maintenance method and system based on a digital twinning technology, and the method comprises the steps: collecting and analyzing various data in a real network based on big data, cloud computing and artificial intelligence technologies, and constructing digital twinning network models in one-to-one correspondence with the real network; various fault scenes are simulated in the digital twin network model, and the probability and position of fault occurrence are predicted through a machine learning algorithm; when a fault occurs in the real network, using the digital twin network model to locate the fault type and position; based on the fault type and position, calling a preset repair strategy library or generating a new repair scheme, and implementing repair through an automatic tool; based on network load prediction, user demand analysis and fault prediction results, simulation and optimization of a resource allocation scheme are carried out by using a digital twin network model, and the method is implemented in a real network. According to the invention, rapid response, accurate positioning and intelligent repair of network faults and dynamic optimal configuration of network resources can be realized.

The invention relates to a power transmission line fault positioning method based on an improved YOLOv8 model and a double-end TWM, and the method comprises the steps: collecting three-phase current transient signals at M and N ends of a line after a fault occurs, carrying out the phase-mode conversion of the three-phase current transient signals through employing a Karen's transformation method, and selecting a traveling wave current line-mode component as a fault traveling wave research object; using a wavelet analysis method to decompose the traveling wave current line mode component to obtain traveling wave generation pictures with different sampling rates; building a Pix2PixHD model and training the Pix2PixHD model; constructing a fault target detection model, and obtaining a time range of a fault traveling wave head by using the fault target detection model; constructing a traveling wave main body segmentation model, and obtaining a traveling wave head maximum value point by using the traveling wave main body segmentation model; and according to the time range of the fault traveling wave head and the traveling wave head maximum value point information, calculating to obtain a line fault point position. According to the invention, accurate line fault positioning is realized, and the problem of low line fault positioning accuracy in the prior art is solved.

The invention discloses a wind power equipment fault diagnosis optimization algorithm, and belongs to the field of wind power equipment management, and the optimization algorithm comprises the following specific steps: I, collecting and preprocessing various operation data of wind power equipment; iI, constructing an equipment fault map and carrying out feature extraction on the preprocessed data; iII, performing fault diagnosis and classification on the real-time data based on the equipment fault atlas; iV, optimizing a fault processing scheme according to the diagnosed fault and the type thereof; v, evaluating the probability of fault occurrence and formulating a corresponding early warning mechanism; according to the method, local optimum can be avoided, the effectiveness of a fault processing scheme is improved, the flexibility and adaptability of the scheme are enhanced, a feasible solution can be quickly generated, the method is suitable for large-scale data processing, the accuracy of fault probability evaluation is improved, comprehensive fault occurrence probability analysis is provided, targeted maintenance strategies are helped to be formulated, and the method is suitable for large-scale data processing. Resource configuration can be optimized, potential faults can be responded in time, the downtime of equipment is shortened, and the operation cost is reduced.

The invention provides a server fault prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining time sequence data corresponding to an operation index when a server operates, dividing the time sequence data corresponding to the operation index into a plurality of time windows, and obtaining a target dynamic feature corresponding to each time window; further predicting the fault probability of the server based on the target dynamic feature corresponding to each time window under the operation index, and outputting a server fault alarm indication when the fault probability is greater than a preset threshold value; according to the method, the fault probability of the server is predicted according to the current time, namely the server operation condition before the current time, so that the possible fault of the server is predicted before the fault occurs.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention provides a data processing method and device, relates to the field of communication, and aims to improve the timeliness of fault prediction of high-risk hardware, improve the response speed of hardware fault prediction, reduce the probability of hardware faults and improve the stability and reliability of the hardware. In the method, a first device obtains log information of first hardware, and the first hardware comprises a plurality of pieces of hardware; the first device obtains a historical probability of fault occurrence of each hardware in the first hardware; the first device sorts the first hardware according to the size of the historical probability to obtain a sorting result, and when the historical probability is higher, the sorting of the first hardware corresponding to the sorting result is higher; and the first device analyzes the log information according to the sorting result from front to back so as to predict the probability that each piece of hardware in the first hardware has the fault.