421results about How to "Improve troubleshooting efficiency" patented technology

The invention relates to the field of vehicle data analysis, in particular to an automobile data analysis method and system based on an intelligent diagnostic instrument. The method comprises the following steps: acquiring vehicle multi-modal monitoring data based on an intelligent diagnostic instrument; operation power change calculation is conducted on the vehicle multi-mode monitoring data, time sequence state feature mining is conducted, and vehicle time sequence state features are obtained; carrying out multi-frequency noise reduction optimization on the vehicle multi-modal monitoring data, and carrying out normalized periodic evolution mining so as to generate normalized vibration periodic evolution characteristics; potential feature association deep mining is carried out on the vehicle time sequence state features and the normalized vibration period evolution features, then association graph modeling is carried out, and a vehicle multi-modal feature knowledge graph is constructed; and abnormal parameter deviation calculation and fault positioning traceability are carried out on the real-time operation parameters of the vehicle based on the vehicle multi-modal feature knowledge graph, so that a potential abnormal fault positioning point is obtained. According to the invention, efficient and accurate vehicle fault risk prediction analysis is realized.

The invention discloses an intelligent centralized control and protection integrated system for a high-low voltage switch cabinet, relates to the technical field of electric power automation, and solves the problems that an existing centralized control system is lagged in data processing, incompatible in communication protocol and difficult in fault positioning. Running parameters and equipment state parameters of each high-low voltage switch cabinet are monitored in real time through a distributed data acquisition module; different communication protocols are automatically identified and adapted through the communication transmission module; real-time data analysis, fault early warning and state evaluation are carried out through a deep analysis module, and an early warning mechanism is triggered when a potential fault is early warned; the fault diagnosis module is used for quickly positioning a fault source and generating a maintenance suggestion, and the remote interaction module is used for providing a graphical user interface, displaying the real-time state of the system and allowing a user to remotely monitor and operate; according to the invention, the performance, compatibility and usability of the system are obviously improved.

The invention discloses a power grid anomaly detection and diagnosis method based on artificial intelligence and topological data analysis. The method comprises the steps of S1, forming power operation data; s2, constructing a topological network model of the power grid based on the physical structure of the power grid; s3, using a first generator to generate abnormal data in the simulation power grid based on the power operation data; s4, optimization abnormal data generation is carried out; s5, embedding the power operation data and topological structure features of the power grid into the anomaly detection model through a graph convolutional network; s6, power grid anomaly detection parameters are dynamically adjusted through the adaptive learning model, so that the anomaly detection model is continuously optimized along with the change of the power grid operation condition; and S7, based on the topological information and abnormal data generated by the generative adversarial network, combining topological data analysis, and positioning a fault node or an abnormal line in the power grid. According to the invention, the accuracy of fault prediction and positioning is improved.

The invention relates to the related technical field of integrated service management, in particular to a digital intelligent technology and software and hardware integrated service management method and platform and a storage medium, and the method comprises the steps: connecting an industrial internet, receiving integrated service requirements, deploying software and hardware based on specifications, fitting a service scene, setting a topological architecture conforming to the specifications, and collecting data to establish a performance model. According to the software and hardware matching topology architecture, a communication architecture is adopted, a monitoring mechanism is set, and abnormal response management is performed on each component and each service in the software and hardware matching topology architecture, so that the technical problems that a service scene is fixed and a static deployment mode is difficult to quickly respond to dynamically changing service requirements in a software and hardware integration process are solved; the technical effects that the industrial internet is connected, the integrated service demand information is received in real time, changes of service demands are flexibly coped with in the software and hardware configuration process, dynamic configuration and adjustment are carried out according to different service scenes and demands, service changes are rapidly responded, and the flexibility and adaptability of resource configuration are improved are achieved.

The embodiment of the invention provides a fault root cause positioning method, a fault root cause positioning device and a related product. The fault root cause positioning method comprises the following steps: acquiring network alarm data and corresponding network topology resource data; performing slicing processing on the network alarm data and the corresponding network topology resource data in a time dimension and a space dimension through a fault causal reasoning model to obtain an initial alarm associated data set; and carrying out screening and reasoning processing on the initial alarm associated data set through the fault causal reasoning model to obtain a fault root cause node and corresponding fault causal relationship data. According to the embodiment of the invention, the fault root cause can be quickly and accurately positioned, so that the fault processing efficiency is improved.

The invention relates to the technical field of railway power supply and distribution, particularly provides an operation and maintenance management method and system for railway power supply and distribution, and is used for solving the problem of low operation and maintenance management efficiency caused by poor accuracy of fault identification and positioning. The method comprises the following steps: receiving equipment operation data of a railway power supply and distribution system collected by a sensor network through the Internet of Things; inputting the data into a pre-trained graph neural network model, and identifying a fault equipment identifier and a fault mode; determining a target fault propagation path according to the fault equipment identifier, the fault mode and the multi-level power supply and distribution knowledge graph; wherein the multi-level power supply and distribution mapping knowledge domain comprises an equipment level mapping knowledge domain, a region level mapping knowledge domain and a system level mapping knowledge domain which are associated with one another; according to the target fault propagation path, a fault severity evaluation model is adopted to predict the fault severity, a corresponding operation and maintenance management report is determined, and corresponding processing is carried out according to the operation and maintenance management report. According to the technical scheme provided by the invention, the operation and maintenance management efficiency is improved.

The invention relates to the technical field of belt state detection, and particularly discloses a belt state detection method based on AI detection identification, and the method comprises the steps: belt deviation detection, deviation reason confirmation and feedback, belt wear detection, wear degree confirmation and feedback, and belt tear detection early warning. Whether the target belt deviates or not is judged according to the running video of the target belt, if the target belt deviates, the deviation reason of the target belt is determined, the abrasion degree of each area of the target belt is determined by constructing the three-dimensional information model of each area, meanwhile, tearing severity early warning is conducted by analyzing the tearing severity coefficient of the target belt, and therefore the tearing severity of the target belt is improved. The method improves the troubleshooting efficiency of belt deviation reasons, improves the accuracy of belt wear degree judgment, accurately evaluates the expansion direction and speed of cracks, provides effective data support for tear prevention and control, and helps to find potential tear risks in time, thereby reducing the possibility of belt tear accidents.

The invention provides an intelligent inspection warning method and system based on panoramic video fusion, and relates to the technical field of intelligent inspection, and the method comprises the steps: obtaining equipment fault first-level warning information through a panoramic video monitoring assembly; according to the abnormal equipment bit number list, constructing an equipment fault relation tree based on the upstream and downstream topological information of the equipment; based on the equipment fault relation tree, extracting first fault equipment, a mother node fault equipment set of the first fault equipment and a child node fault equipment set of the first fault equipment; based on the instrument monitoring state list, performing fault traceability analysis on the first fault equipment and the parent node fault equipment set to generate fault source equipment; adding the fault source equipment into a fault source equipment group; and generating an intelligent inspection warning signal according to the fault source equipment group. The technical problem that in the prior art, accurate traceability of the fault equipment is difficult to achieve is solved, and the technical effects of accurately positioning the fault source equipment and improving the alarm reliability are achieved.

The invention belongs to the technical field of artificial intelligence computing power resource management, and particularly relates to an AI computing power resource monitoring and predicting system and method based on data driving. The acquired computing power resources are sent to an observability analysis module to perform distributed tracking on various application services, and a generated performance flame map is sent to a data integration and output module; meanwhile, protocol analysis is carried out on the obtained computing power resources, full-stack performance indexes are collected to serve as historical computing power resource use data to be sent to the computing power demand prediction module for modeling and prediction, and an obtained computing power resource demand prediction result is sent to the data integration and output module; and the data integration and output module gathers all the data together and injects a uniform label to finally provide a basis for resource scheduling. According to the invention, zero-intruding panoramic monitoring is realized, the coverage range is wide, and the use condition of computing power resources can be mastered more comprehensively by various application services.

The invention discloses a method and a system for boundary scan testing and effective positioning of a short circuit fault. The method comprises the following steps: acquiring first data corresponding to a short circuit fault to be subjected to boundary scan testing and positioning in real time; wherein the first data is a connection relationship of a to-be-tested connection network; based on the first data, respectively generating second data and third data corresponding to a to-be-tested connection network; according to the second data and the third data and in combination with the first data, performing binary processing on level data sent in the to-be-tested connection network; according to the transmission state of the level data in the to-be-tested connection network, fourth data corresponding to the to-be-tested connection network is generated; wherein the fourth data is short-circuit fault data of the to-be-tested connection network; according to the method, the system and the platform corresponding to the method, the short-circuit faults can be clearly checked among the connected networks, and the short-circuit fault checking efficiency is improved.

The invention relates to the technical field of power maintenance, and discloses a power distribution network-oriented single-phase earth fault AI comprehensive research and judgment system, which integrates a power distribution network monitoring module, a fault diagnosis equipment module and an AI comprehensive research and judgment module. The system can accurately judge the type, position and severity of the single-phase earth fault by monitoring and collecting all-dimensional data of the power distribution network in real time and combining intelligent analysis of the AI comprehensive research and judgment module, and the accuracy of fault positioning and isolation is remarkably improved. The AI comprehensive research and judgment module adopts a Markov decision process for modeling, and a deep reinforcement learning algorithm is used for training a research and judgment model, so that rapid fault positioning, isolation and power supply recovery are realized, the fault processing efficiency and the power supply recovery speed are effectively improved, and the power failure time and the influence on users are reduced.

The invention provides an intelligent power grid safety early warning system and a monitoring method in the technical field of intelligent power grids, which are used for solving the problem of safety early warning and monitoring function defects of the existing imperfect intelligent power grids, and comprise an information acquisition unit, a data management unit, an abnormity analysis unit and a safety early warning unit, the method comprises the steps of collecting power grid real-time data and line equipment data; constructing an adjacent matrix and a target matrix, calculating and analyzing to obtain a node weight and a target correlation degree, and analyzing by using a discrete model to obtain a dispersion index; a data evaluation value is obtained, and a data abnormal value is obtained through analysis; and generating intelligent power grid safety early warning information and a disaster recovery strategy according to the data abnormal value and the line equipment data. By constructing an intelligent power grid monitoring and early warning system, power grid equipment with potential safety problems is predicted in real time, the operation and maintenance efficiency is improved, the method can be suitable for power grid systems of different scales, the influence of faults on power grid operation is reduced, and large-area power failure events are effectively prevented.

The invention discloses an on-line discharge capacity checking system of a station DC storage battery pack, and relates to the technical field of power systems. According to the method, voltage, temperature and internal resistance parameters of the storage battery in the continuous discharging process are continuously collected and are respectively analyzed to obtain a voltage evaluation index, a temperature evaluation index and an internal resistance evaluation index of the storage battery in the continuous discharging process, and the voltage evaluation index, the temperature evaluation index and the internal resistance evaluation index are further extracted; the method comprises the following steps: setting respective weight coefficients, multiplying and summing to obtain a health assessment index, presetting a threshold index of the health assessment index, comparing the health assessment index of each storage battery with the threshold index, judging that the storage battery is unqualified if the health assessment index is higher than the threshold index, and judging that the storage battery is qualified if the health assessment index is higher than the threshold index, thereby comprehensively assessing the use state of the storage battery. The problems that in the prior art, the health state of the storage battery is difficult to comprehensively and accurately assess, and multi-parameter comprehensive analysis and quantitative assessment means are lacked are solved.

The invention provides an easy-to-assemble backheating device for a solid elastic card, which comprises a sleeve, a plurality of solid elastic card material plates stacked in the sleeve and a pressure head for applying stress to the solid elastic card material plates, and the sleeve is composed of a plurality of parts and is used for inspecting or assembling and disassembling the plurality of solid elastic card material plates when being unfolded; each solid elastic clamping material plate is provided with a flow cavity after being stacked, a heat-conducting medium exchanges heat with the solid elastic clamping material plates through the flow cavities when heat or cold is generated, and the heat-conducting medium flows into a heat exchanger through a liquid through hole in the pressure head for heat exchange. The sleeve is designed to be a plurality of detachable parts or folding parts, during assembly, a plurality of solid elastic clamping material plates can be conveniently placed in, alignment is convenient, in the loading and unloading process, even if a certain solid elastic clamping material plate goes wrong, opening and inspection are convenient, and therefore the assembly efficiency is improved, and the fault solving efficiency is improved.

The invention relates to the field of municipal sewage pipe networks, and discloses a sewage pipe network key node on-line monitoring index-based pipe network problem troubleshooting method, which comprises the steps of basic data collection, pipeline data information integration, and informatization integration of pipeline topology information and elevation data. Making a dynamic flow diagram of a pipe network, and making the dynamic flow diagram of the pipe network according to the elevation data and a field investigation result; a monitoring scheme and drainage partitions are formulated, monitoring points with high practicability, representativeness and feasibility are selected according to a pipeline flow direction relation graph and a monitoring point layout principle, and diagnosis partitions are divided according to the monitoring points; checking the monitoring data, wherein the checking comprises flow checking, liquid level checking and water quality calibration. Technical breakthrough is made in the aspect of pipe network troubleshooting, the risk troubleshooting range can be narrowed through information integration, data analysis and problem diagnosis, problem types and nodes can be diagnosed, the efficiency of urban pipeline troubleshooting is improved, and important practical value is brought to urban drainage pipe network troubleshooting work.

The invention relates to the field of video inspection, in particular to an active video inspection system based on Internet of Things driving, which comprises a current analysis module, an image input module, an image input detection module, an image output detection module and an audio detection module. According to the method, the current of the conference room equipment is monitored, abnormal equipment is found in time, rapid fault diagnosis is achieved, the effectiveness of the video stream is effectively judged by obtaining and processing the video stream and analyzing the grayscale image, it is ensured that the input picture is always in an available state, the blank screen phenomenon and picture distortion are avoided, and the user experience is improved. The image output detection module verifies the display effect, ensures the accuracy of an output picture and determines an abnormal area when the output picture is abnormal, the audio detection module plays a test audio and collects feedback, the sound receiving effect of a microphone is effectively evaluated, the conference room equipment is automatically and actively inspected, the equipment state is continuously monitored and evaluated, and the sound receiving efficiency is improved. Potential faults can be found and prevented in time, and the sudden fault risk of equipment is remarkably reduced.

The invention relates to the field of power system operation and maintenance, in particular to a three-dimensional visualization method applied to a substation operation and maintenance system, which comprises the following steps of: acquiring a three-dimensional model and position information of substation equipment, constructing an integral three-dimensional model of a substation by utilizing technologies such as laser scanning and photogrammetry and combining positioning technologies such as GPS (Global Positioning System) and RTK (Real-Time Kinematic); a sensor is installed on equipment, operation parameters such as voltage, current, temperature and vibration are collected in real time and transmitted to a three-dimensional visualization system through a wireless or wired communication network, association display of real-time data and a three-dimensional model is achieved, and the system provides a three-dimensional view capable of rotating, zooming and translating. The operation state and fault alarm information of the equipment are displayed in real time, and historical data query and alarm log functions are supported. By analyzing real-time and historical data, the system can identify and predict potential faults of the equipment, generate maintenance plans and optimization suggestions, and support remote collaboration and fault handling.

The invention relates to an intelligent operation and maintenance method applied to an operation and maintenance platform, and the method comprises the steps: obtaining the operation data of a to-be-tested device; judging whether the to-be-tested equipment is abnormal or not based on the operation data, and if the to-be-tested equipment is abnormal, determining the operation data as to-be-tested fault data; inputting the to-be-detected fault data into a pre-trained fault analysis model, and outputting a target processing report corresponding to the to-be-detected fault data, the fault analysis model comprises a fault detection module for performing fault type detection on to-be-detected fault data, a retrieval module for retrieving a target solution corresponding to the to-be-detected fault data from a preset operation and maintenance knowledge base, and a scheme generation module for generating a corresponding target processing scheme according to the to-be-detected fault data; and sending the target processing report to a management terminal corresponding to the to-be-tested device. The problems that in the prior art, an intelligent operation and maintenance system excessively depends on manual processing and experience judgment, so that the fault processing efficiency is low, the response speed is low, and the error rate is high are solved.

The invention provides a desulfurization absorption tower system monitoring method, device, equipment and medium, and belongs to the technical field of thermal power desulfurization. The method comprises the following steps: acquiring numerical values of a plurality of monitoring devices of the desulfurizing absorption tower system; based on the emergency level of the data type of the numerical value of each monitoring device and the operation state of each monitoring device, evaluating the numerical value of each monitoring device to obtain a score of the numerical value of each monitoring device; calculating the average value of the scores of the numerical values of the monitoring devices to obtain the score of each sub-device of the desulfurizing absorption tower system, and calculating the average value of the scores of each sub-device of the desulfurizing absorption tower system to obtain the score of the desulfurizing absorption tower system; and the score of the numerical value of each monitoring device, the score of each sub-device of the desulfurization absorption tower system and the score of the desulfurization absorption tower system are transmitted to a DCS system for layered and graded display. According to the invention, real-time monitoring and early warning, efficient fault diagnosis, data integration and sharing and visual display are realized, and the equipment operation management efficiency and operation convenience are improved.

The invention provides a fault detection method and device for a charging pile and electronic equipment, and relates to the technical field of detection. According to the method, operation data of a charging pile during working are obtained, the operation data comprise multiple sub-operation data, and the sub-operation data are any one of temperature, voltage, current, power factor, frequency, harmonic content and insulation resistance; inputting the operation data into a preset support vector machine model to obtain a detection result; if it is determined that the detection result indicates that abnormal sub-operation data exists in the multiple pieces of sub-operation data, it is determined that potential faults exist in the charging pile; determining a fault level based on the abnormal sub-operation data; and triggering an early warning mechanism according to the fault level. By implementing the technical scheme provided by the invention, the accuracy of fault detection of the charging pile is improved.

The embodiment of the invention provides a task scheduling method, electronic equipment, a computer storage medium and a computer program product. The task scheduling method comprises the steps of obtaining a plurality of tasks to be scheduled; the plurality of tasks comprise an upstream task composed of a plurality of upstream task fragments and a downstream task composed of a plurality of downstream task fragments; determining a task fragment dependency relationship, wherein the task fragment dependency relationship represents a dependency relationship between each upstream task fragment and each downstream task fragment; in response to completion of execution of the current upstream task fragment, allocating a task executor to a target downstream task fragment depending on the completed upstream task fragment, so that the task executor executes the target downstream task; the target downstream task fragment is determined through the task fragment dependency relationship. According to the embodiment of the invention, the resource utilization rate of the task executor can be improved, the task processing efficiency is improved, the task execution time is shortened, and the task processing delay is reduced.

The invention belongs to the field of photovoltaic power generation, relates to a fault diagnosis technology, and aims to solve the problem that the reliability of detection results of all photovoltaic arrays cannot be ensured in the prior art, in particular to an intelligent fault diagnosis system for a photovoltaic power generation module based on a sensor technology. Comprising a data statistics module, a fault monitoring module, a fault diagnosis module and a troubleshooting module. The data statistics module is used for carrying out statistics and analysis on power generation parameters of the photovoltaic array: marking the photovoltaic array as a statistical object, generating an analysis period and obtaining the power generation parameters of the statistical object in the analysis period at the end moment of the analysis period; the power generation data of the photovoltaic array in different illumination environments can be collected in the normal state of the photovoltaic array, the power generation parameters are formed, the power generation parameters serve as the running state judgment standard of the photovoltaic array, and when fault monitoring is carried out, the fault monitoring accuracy is improved. And fault monitoring is carried out by taking the power generation parameters of the same photovoltaic array in the similar illumination environment as a reference.

The invention discloses a power distribution automation terminal operation and maintenance method based on a 104 protocol, and belongs to the field of electric power automation, and the method specifically comprises the steps: employing the IEC 104 protocol to collect the state data of power grid equipment through a power distribution automation terminal, and transmitting the state data to a main station system in real time; the master station system processes the received data by using an intelligent fault diagnosis model to realize accurate classification and grading of faults; once a fault or abnormity is detected, an early warning mechanism is triggered immediately, and a customized fault notification is generated and sent to operation and maintenance personnel; and operation and maintenance personnel perform remote operation by using the intelligent operation and maintenance APP, quickly respond and process faults, and then feed back a processing result to the master station system, so that instant updating and closed-loop management of the state of the power grid equipment are realized, and the operation and maintenance efficiency and safety of the power grid are improved.

The invention discloses a method and system for monitoring components in a GCB control cabinet based on state recognition, and relates to the technical field of monitoring and maintenance of an electric power system.The method comprises the steps that state information of the components in the GCB control cabinet is collected, and all the collected state information is transmitted to a server to be stored and preprocessed; based on an abnormal recognition algorithm of the decision-making unit, analyzing components in the GCB control cabinet and an environment state; and based on the abnormal state threshold value and an alarm unit, carrying out abnormal alarm and formulating an adjustment strategy. According to the method, through integrated monitoring, intelligent analysis and automatic response strategies, data cleaning, format conversion and standardized processing, the data quality is improved, a quantitative decision-making basis is provided for a decision-making unit, the intelligent level of decision-making is improved, the operation safety and reliability of the GCB control cabinet are improved, and the GCB control cabinet is suitable for popularization and application. The fault rate is reduced, the maintenance efficiency is improved, and revolutionary progress is brought to the field of industrial automation.

The embodiment of the invention discloses a fault processing method and device for smart home equipment, a storage medium and a smart home gateway, and relates to the field of household appliance control. The working state of the smart home equipment is detected in real time; when the working state of the smart home device is an abnormal state, obtaining a device identifier of the smart home device, and querying latest device field data in a block chain network according to the device identifier; verifying whether the queried equipment field data is tampered or not; and if not, sending the equipment field data to the smart home equipment, and indicating the smart home equipment to recover to a normal state according to the equipment field data. The data integrity and credibility are guaranteed by utilizing the block chain technology, an intelligent equipment recovery mechanism is realized, the reliability, convenience and safety of the intelligent home system are remarkably improved, and better intelligent home experience is provided for users.

The invention discloses a railway external environment hidden danger intelligent identification method based on an unmanned aerial vehicle inspection video, and the method comprises the steps: carrying out the preprocessing of data shot by an unmanned aerial vehicle, and forming a sample data set; training a target detection network by using the sample data set to obtain an optimal weight parameter; judging a target detection model applicable to a current scene according to an environment image acquired by the unmanned aerial vehicle in real time, and acquiring an identification frame of a risk target by using the trained target detection network; the identification frame comprises pixel coordinates of the risk target and category information of the risk target; constructing a rotation matrix from the pixel space coordinate system to the ground auxiliary coordinate system; and according to the identified pixel coordinates of the risk target, obtaining the Gaussian plane coordinates of the position of the unmanned aerial vehicle, and calculating the geographic coordinates of the position of the risk target by using the constructed rotation matrix. According to the invention, a set of efficient and intelligent troubleshooting and treatment system scheme for railway external environment hidden dangers is constructed, and the accuracy of hidden danger identification and the precision of spatial positioning are improved.

The invention discloses an equipment operation and maintenance management system and method. Multi-modal real-time operation data is acquired through the multi-modal data acquisition module, so that the operation state of the target equipment in the whole life cycle can be reflected more comprehensively, and the accuracy and timeliness of fault detection are improved. The fault early warning module carries out deep analysis on the real-time operation data by using a pre-trained fault early warning model supporting multi-modal data analysis in combination with historical operation data of the target equipment and pre-configured risk early warning conditions, can accurately judge whether the real-time operation data is normal or abnormal operation data, and improves the accuracy of the real-time operation data. And a corresponding fault diagnosis result is given, so that potential faults can be found in advance, and the fault processing efficiency is improved. The fault processing module automatically matches and obtains a corresponding emergency processing scheme by using the pre-trained fault processing model and historical operation and maintenance case knowledge in the historical fault knowledge base, so that the burden of operation and maintenance personnel is reduced, and the accuracy and efficiency of fault processing are improved.

The invention relates to the technical field of power system diagnosis, in particular to a power distribution network single-phase earth fault multilateral collaborative diagnosis and positioning method, which comprises the following steps: S1, collecting current and voltage signals in a power distribution network in real time through a sensor network; s2, preprocessing the current and voltage signals; s3, analyzing the unbalance degree of the current and the abrupt change point of the voltage, and identifying whether a single-phase earth fault occurs or not; s4, calculating the propagation time difference of the fault signal by using a time difference of arrival method, and preliminarily estimating the position of the fault; s5, correcting a fault position to locate a specific fault point; s6, generating a fault positioning report; and S7, according to a fault positioning result, triggering a processing measure to send a maintenance instruction to related personnel. According to the invention, through combination of multi-data source analysis and a power grid topological structure, an accurate fault positioning and automatic processing flow is provided, the fault detection precision and the processing efficiency are effectively improved, and stable operation of a power grid is ensured.

The invention discloses a distribution network self-healing management method and system, and relates to the technical field of self-healing control of a distribution network, and the method comprises the steps: generating a fault log; automatically generating a fault log line attribute based on the line attribute managed by the line ledger; an operator initiates a self-healing action analysis application; auditing the self-healing action analysis list; automatically calculating a summary index based on the self-healing action analysis information; and generating a self-healing application evaluation criterion based on the self-healing action analysis summary index and the self-healing action analysis information. According to the method, by automatically generating the fault log and matching the line attributes, manual input errors are reduced, the response speed of fault processing is improved, the self-healing line investment, change and back operation application process is standardized, the operation normalization and accuracy are ensured, the management complexity and operation errors are reduced, and the management efficiency is improved. The automatic recording and analysis of the self-healing action are realized, the time required by manual analysis is reduced, and the fault processing efficiency is improved.

The invention relates to the field of communication warning, in particular to a real-time monitoring system and method based on OLT equipment warning, and the method comprises the following steps: S1, collecting equipment response time, packet loss rate, state information of OLT equipment and disconnection data of a user, and carrying out the preprocessing of the collected data; s2, analyzing and processing the collected data, and identifying a complex fault mode and a multi-factor problem; s3, alarm information is generated for the Pi ng measurement alarm, the port abnormity alarm, the optical modem offline alarm and the comprehensive alarm, operation and maintenance personnel are informed of the generated alarm information through automatic processing, and processing suggestions are provided; and S4, real-time state display is performed through the data acquisition module, and comprehensive data display, historical data query and data interaction are performed through the data processing module. The fault response speed can be improved, the network stability is enhanced, the operation and maintenance efficiency is improved, and the user experience is improved.