586results about How to "Improve operation and maintenance efficiency" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention provides a distributed system fault positioning diagnosis method and system based on log analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting call data between micro-service nodes, constructing a service dependency graph, and calculating the influence weight of each node as a fault source; and modeling the service dependency relationship graph by using a graph convolutional neural network, and constructing a fault propagation prediction model. And when an abnormal node is detected, inputting the node into the prediction model, calculating a fault propagation path and an influence range, outputting an influenced service node list and an influence degree thereof, carrying out risk grade division and alarm, and meanwhile, visually displaying the fault propagation path. According to the method, the fault source can be quickly and accurately positioned, the fault propagation influence is predicted, and the reliability and stability of the distributed system are improved.

The invention discloses a data-driven material conveyor fault early warning method and system, and relates to the field related to data early warning, and the method comprises the steps: arranging a plurality of sensors for real-time collection, obtaining a real-time operation data set of a material conveyor for multi-dimensional analysis, determining multi-dimensional operation data for time series decomposition, extracting a plurality of operation features, and carrying out the early warning of the faults of the material conveyor; learning training is carried out according to the historical operation record log to obtain a fault prediction model, the real-time operation data set is synchronized to the fault prediction model for prediction calculation, and the operation health score of the material conveyor is determined to carry out fault judgment on the material conveyor. And determining a fault risk level, generating an early warning signal, and sending the early warning signal to a remote terminal for intelligent early warning feedback of the fault. The technical problem that intelligent maintenance is difficult to achieve due to the fact that fault monitoring of the material conveyor is not timely and the early warning precision is low is solved, real-time monitoring and intelligent feedback of the equipment state are achieved through data-driven fault prediction and real-time early warning, and the technical effect of improving the operation and maintenance efficiency is achieved.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention discloses an urban tunnel intelligent safety operation and maintenance method and system combined with multiple data, particularly relates to the field of intelligent traffic operation and maintenance, and is used for solving the problems of prediction deviation and congestion management caused by tunnel flow fluctuation. In combination with traffic parameters of a multi-point detector, a wave similarity analysis model is utilized to accurately evaluate a propagation path and a target section of fluctuation; meanwhile, by means of a time sequence superposition correction mechanism, a historical fluctuation rule and real-time data are fused, a traffic flow transmission prediction curve after dynamic correction is generated, and balance between physical constraint and prediction precision is achieved; finally, key indexes are output through dynamic updating of the flow prediction model, efficient linkage of signal coordination and induction management and control modules is guided, congestion accumulation and traffic risks caused by fluctuation propagation can be reduced to the maximum extent, the tunnel operation and maintenance efficiency is optimized, and the traffic safety guarantee capacity is enhanced.

The invention discloses a self-adaptive optimization method for a fan control strategy. The method comprises the following steps: acquiring real-time data of a fan, health state data of equipment in the fan and meteorological data around the fan; performing data preprocessing and fusion on the acquired real-time data, the equipment health state data and the meteorological data to generate processed data; based on the processing data, constructing a deep neural network model, and correcting a power curve of the traditional fan according to the real-time working condition through the constructed deep neural network model; adjusting control parameters of the fan according to the corrected power curve by using a particle swarm optimization algorithm; through a machine learning algorithm, real-time analysis is carried out on the operation data of the fan, and early warning is carried out on potential faults; according to the actual operation effect and the potential fault early warning information of the draught fan, the feedback mechanism is combined, the draught fan control strategy is optimized, the draught fan control strategy can be accurately and dynamically adjusted, and it is ensured that the draught fan is always kept in the optimal working state.

The invention relates to the technical field of photovoltaic power generation, and discloses a photovoltaic power generation capacity time sequence prediction method based on an improved Transform model, and the method comprises the following steps: S1, carrying out the cleaning and standardization processing of environment data and power generation capacity data; s2, extracting local features from the input multivariate time sequence data by using a convolutional layer; s3, introducing a new dynamic weight adjustment module, and dynamically adjusting the attention weight according to the correlation between the environmental factors and the power generation amount; s4, proposing a staged decoding mechanism, reducing the calculation amount through preliminary prediction and refined decoding, and paying attention to important information; s5, performing end-to-end training on the model in combination with the optimized attention mechanism and the generative decoder; and S6, using the trained model to carry out generating capacity prediction on new data, and using an evaluation index to evaluate a prediction result.

The invention provides a graphical construction and edge deployment method for configuration software development for a micro-service architecture, which comprises the following steps of: constructing an interface and logic of configuration software by adopting a graphical interface through operations such as drag-drop, connection and the like; establishing a graphical component library comprising a plurality of predefined micro-service components, wherein the predefined micro-service components comprise data acquisition, processing, storage, display and the like; the predefined micro-service component supports conversion of the graphical component into an executable micro-service code, and configuration software is constructed; deploying the micro-service of the configuration software at a position close to a data source in combination with an edge computing node deployment strategy; the edge deployment framework automatically completes deployment and starting of the micro-service according to the configuration file and supports automatic deployment, monitoring and upgrading of the micro-service; the states of the edge computing nodes are monitored through a graphical interface, and remote configuration and management are carried out; and performing real-time viewing, log analysis, troubleshooting and the like on the node state. The development efficiency is improved.

The invention relates to the technical field of photovoltaic construction, and discloses an intelligent monitoring device and method for photovoltaic construction, and the device comprises a data collection module which collects the equipment data of a photovoltaic power station in real time; the real-time monitoring module is used for displaying the operation and state of the equipment in the forms of graphs and tables; the remote monitoring module is used for remotely monitoring the overall operation condition; the data storage module is used for storing sunlight amplitude, temperature, wind speed meteorological information and operation parameters of the module; and an early warning module. Through real-time monitoring and early warning, problems can be found and solved in time, system fault time is reduced, power generation efficiency is improved, maintenance cost caused by delayed problem finding is reduced, power station operation cost is reduced, through an intelligent scheduling module, operation and maintenance processes are optimized, operation and maintenance efficiency is improved, manpower resource waste is reduced, and through data analysis and report service, the system can be used for monitoring and early warning in real time. Decision basis is provided for work development of the photovoltaic power station, and the management level is improved.

The invention relates to the field of medical equipment, and discloses a medical intelligent multi-layer protective gas conveying system, method and device and electronic equipment, and the system comprises a data collection module which is used for collecting gas flow, pressure, temperature and leakage state information in real time; the path optimization module is used for dynamically optimizing a gas conveying path; the control execution module adjusts the opening degree of the valve and the pumping power according to the optimization result; the feedback updating module feeds back the execution effect after path switching to the optimization model, and dynamically adjusts optimization parameters; the monitoring platform module visually displays the operation state of the system and provides alarming and fault positioning functions under abnormal conditions. Through closed-loop optimization and intelligent regulation and control, the energy consumption and leakage risk are reduced, the gas conveying efficiency and stability are improved, meanwhile, the real-time requirement in a complex medical scene is met, and the system is suitable for conveying scenes of medical gas such as oxygen and anesthetic gas.

The invention provides a steam turbine set vibration fault diagnosis method and system, and belongs to the technical field of thermal power plant fault diagnosis. The method comprises the following steps: inputting a to-be-identified vibration signal of a turboset into a trained turboset vibration fault diagnosis model for vibration fault diagnosis; the training rule of the steam turbine set vibration fault diagnosis model comprises the following steps: extracting a discrete wavelet transform coefficient from a steam turbine set vibration signal to obtain a DWTC image, and converting the steam turbine set vibration signal into a vibration signal image; taking the DWTC image and the vibration signal image as inputs of two branches of a double-flow convolutional neural network to perform high-level feature learning, and fusing high-level feature images output by the two branches of the double-flow convolutional neural network after preset processing; and inputting the fused image features obtained by fusion into a preset classifier, and outputting a corresponding turboset vibration fault type. The method and the system have the advantages of high accuracy, good generalization ability and strong robustness.

The invention discloses a dynamic harmonic filtering control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting a voltage signal and a current signal of a power grid in real time; performing spectral analysis on the collected signals, and extracting harmonic characteristics; based on the harmonic characteristics, generating a compensation signal and optimizing the compensation signal; dynamically adjusting filtering parameters, and injecting an optimized compensation signal into a power grid through a filter module; uploading a system operation state in real time, and detecting and marking an abnormal state; according to the invention, rapid response and efficient suppression of power grid harmonics can be realized, the adaptability and filtering precision of multi-band harmonics are significantly improved, and the total distortion rate of the harmonics is greatly reduced, so that the quality of electric energy is optimized. The system operation efficiency and stability are improved, the anti-interference capability is strong, and the system is suitable for a complex power grid environment; the system can monitor the operation state in real time, automatically give an alarm and mark abnormity, the reliability and safety of power grid operation are improved, and the maintenance cost is reduced.

The invention provides a method for achieving Kubernetes cluster service public network access through a P2P network and a gateway. The method is characterized in that the method is based on a Service Controller assembly, a gateway system, a P2P virtual network and a DNS system; comprising the following steps: S1, deploying a gateway system, and configuring domain names and extensive domain name certificates; s2, deploying a P2P system; s3, configuring a routing forwarding rule for the P2P node of the K8S cluster through the P2P server; s4, the resource change in the Kubernetes cluster is monitored in real time; s5, performing gateway configuration; according to the invention, through the P2P virtual network and the point-to-point distributed architecture, the risk of single point failure is eliminated, through end-to-end encryption and dynamic key negotiation, the risk of certificate leakage is prevented, and through a multi-level security mechanism, the overall security of the system is enhanced, the network forwarding level is reduced, and the service access delay is reduced.

The invention provides an electric power safety protection method and system for switch cabinet control, and relates to the technical field of electric power safety, and the method comprises the steps: arranging a vibration sensor, and collecting Q joint point vibration monitoring data sequences and P mechanical control point vibration monitoring data sequences; arranging an infrared thermal imager to collect an infrared temperature distribution diagram sequence; traversing the vibration monitoring data sequences of the Q joint points and the P mechanical control points to perform vibration anomaly analysis to obtain a first abnormal joint point set and a first abnormal mechanical control point set; abnormal temperature point identification is carried out according to the infrared temperature distribution diagram sequence, and an abnormal temperature point position set is obtained; and integrating all the abnormal positions to obtain an abnormal position point and sending the abnormal position point to a worker for safety detection. The technical problem that in the prior art, operation monitoring is carried out on the switch cabinet only from a single dimension, an abnormal position is difficult to locate accurately, and consequently the safety detection accuracy of the switch cabinet is insufficient is solved, and the safety protection efficiency and accuracy of the switch cabinet are improved.

The invention provides a pipeline defect assessment method and system based on multi-source data fusion, and the method comprises the following steps: collecting the data of the operation state of a pipeline body through a related collection device, and marking the state and fault type of the pipeline, so as to construct a training set and a test set; and preprocessing the static data of the pipeline, wherein the preprocessing comprises data cleaning, standardization and feature extraction. And optimizing the extreme learning machine by using a bird flock algorithm, analyzing static data and evaluating pipeline defects. Preprocessing the image data of the pipeline body, wherein the preprocessing comprises image conversion, enhancement and denoising; and evaluating the circumferential weld defect based on the pre-trained VGG16 model. And performing decision-level fusion on a defect evaluation conclusion obtained by static data analysis and circumferential weld image analysis by using a D-S evidence reasoning algorithm to obtain a final evaluation result. The method effectively improves the accuracy and reliability of pipeline defect evaluation, and has wide application prospects and important practical value.

The invention provides a wind power operation and maintenance fault diagnosis method and system, and belongs to the technical field of wind power operation and maintenance. The method comprises the following steps: constructing a wind power operation and maintenance knowledge base based on multi-source historical operation data of target wind power equipment and operation knowledge experience data of the target wind power equipment; training based on a wind power operation and maintenance knowledge base to obtain a fault diagnosis model; wherein the fault diagnosis model comprises a plurality of feature extraction combination layers used for extracting data features of corresponding layers in the multi-source historical operation data of the target wind power equipment; inputting the multi-source real-time operation data, collected in real time, of the target wind power equipment into the fault diagnosis model, so as to extract the data features of the corresponding layers in the multi-source real-time operation data of the target wind power equipment based on each feature extraction combination layer, and extracting the multi-source real-time operation data of the target wind power equipment based on the data features of each layer; and determining a wind power operation and maintenance fault diagnosis result corresponding to the target wind power equipment. Intelligent operation and maintenance management of the wind power equipment is realized, the operation and maintenance efficiency and quality are improved, and the operation and maintenance cost is reduced.

The invention provides a service fault recovery method and system for a micro-service platform, a storage medium and equipment. The method comprises the following steps: analyzing a service state in real time, and collecting service state index data; determining a fault service, collecting fault state data, and obtaining structured data of service call graph information based on the service state index data and the fault state data; constructing a reinforcement learning fault recovery model, inputting the structured data of the service call graph information into the reinforcement learning fault recovery model, and training the reinforcement learning fault recovery model based on known historical data; and generating a recovery strategy of the fault service by using the trained reinforcement learning fault recovery model, and executing the recovery strategy to recover the fault. According to the service fault recovery method, the fault recovery efficiency is improved.

The invention discloses a micro-grid intelligent operation and maintenance controller and method, and relates to the technical field of micro-grid operation and maintenance. The intelligent operation and maintenance subsystem is formed by interactively connecting a plurality of cluster servers, each cluster server adopts a small convolutional neural network to establish a fault information set corresponding to each front-end device, and the cluster servers are utilized to train and learn a convolutional neural network model so as to obtain a small convolutional neural network and a large convolutional neural network; therefore, the state data set is identified so as to judge whether fault information exists or not. The micro-grid intelligent operation and maintenance controller and the micro-grid intelligent operation and maintenance method have good openness and compatibility, can be suitable for various micro-grids composed of different primary devices, can automatically judge micro-grid faults and carry out corresponding fault processing under the unattended condition, improve the micro-grid fault processing efficiency and reduce the fault processing cost. The dependence of micro-grid operation on operation and maintenance personnel is reduced, and the operation stability of the micro-grid is improved.

The invention discloses a power transformation equipment fault prediction system and method based on multi-modal data, and the system comprises a data collection module which is used for obtaining the multi-modal data of power transformation equipment at the current moment based on a plurality of sensors; the data preprocessing module is used for performing preprocessing and standardization operation based on the multi-modal data at the current moment to obtain standardized data at the current moment; the feature extraction module is used for performing feature extraction based on the standardized data at the current moment to obtain a feature vector at the current moment; the real-time detection and prediction module is used for performing fault prediction based on the feature vector at the current moment in combination with a pre-trained fault prediction model to obtain a fault prediction result; according to the invention, the obtained multi-modal data can be used for monitoring the power transformation equipment at different angles and dimensions, wider fault modes and abnormal conditions are covered, and the operation and maintenance efficiency is improved; according to the method, high-dimensional data fusion is carried out, so that the accuracy and reliability of fault recognition can be remarkably improved, and the situations of false detection and missing detection are reduced.

The invention belongs to the technical field of photovoltaic anomaly recognition, and particularly relates to a photovoltaic array multi-level aging recognition processing method, device and equipment and a medium. The method comprises the steps of obtaining a voltage change rate and a current change rate of a maximum power point of the photovoltaic array, and inputting the voltage change rate and the current change rate into a first fault identification model based on a support vector machine to obtain an abnormal aging degree of the photovoltaic array. In the training process of the first fault recognition model, training set data is obtained based on a photovoltaic array simulation model, simulation resistors are arranged in the model to simulate different aging degrees, and parameters of a support vector machine are optimized by using a dynamic adaptation cuckoo search algorithm. And when light, moderate or heavy anomalies are identified, the voltage change rate and the current change rate of the maximum power point of the photovoltaic string in the abnormal aging photovoltaic array are further obtained, the aging degree of each photovoltaic string is obtained by using a second fault identification model, and according to the aging degree and the switch matrix, the maximum power point voltage change rate and the maximum power point current change rate of each photovoltaic string are obtained. And dynamic self-adaptive compensation operation and maintenance measures of different duty ratios of the compensation string corresponding to the multi-level fault are adopted.

The invention discloses an intelligent operation and maintenance service method and system for an electronic port, and relates to the technical field of operation and maintenance services of the electronic port, and the system comprises a data collection module which is used for monitoring the operation state of equipment in the electronic port in real time, and obtaining the operation parameters of the equipment; and the data analysis module is connected with the data acquisition module, and performs preprocessing based on the acquired operation parameters to obtain lossless parameters. According to the intelligent operation and maintenance service method and system for the electronic port, reasonable resource allocation, workload balance of operation and maintenance personnel and shortening of processing time are realized, the operation and maintenance efficiency is improved, it is ensured that the system can be rapidly processed when a fault occurs, the influence of the fault on electronic port business is reduced, and the service quality of the electronic port is improved. Reasonable resource allocation, workload balance and processing time shortening can be realized, and the operation and maintenance efficiency and the system performance are improved.

The invention relates to the technical field of electric power safety monitoring, in particular to an electric power inspection well safety early warning method and system, and the method comprises a data collection step, a modeling step, a model training step, a prediction step, a judgment step and a fault early warning step. The system comprises a data acquisition module, a modeling module, a model training module, a prediction module, a judgment module and a fault early warning module. The electric inspection well monitoring system has the effects of monitoring the electric inspection well in time and reducing problems.

The invention is suitable for the technical field of photovoltaic operation and maintenance, and particularly relates to a distributed photovoltaic power station operation and maintenance method and equipment, and the method comprises the steps: obtaining image data and operation data of a distributed photovoltaic power station, and providing a detailed data basis for subsequent analysis; according to the image data of the distributed photovoltaic power station, the first operation and maintenance parameter of the distributed photovoltaic power station is determined, and the physical anomaly of the photovoltaic module is identified by evaluating the abnormal condition in the image data; according to the operation data of the distributed photovoltaic power station, second operation and maintenance parameters of the distributed photovoltaic power station are determined, and the problem of electrical performance or thermal performance is detected by evaluating an abnormal mode in the operation data; based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, operation and maintenance management is carried out on the distributed photovoltaic power station, the operation and maintenance management level of the photovoltaic power station is improved, the operation and maintenance efficiency and accuracy are enhanced, and potential problems can be found and solved in time.

The invention relates to a fault analysis method and device based on a large model, computer equipment and a computer readable storage medium. The method comprises the following steps: receiving fault information; retrieving the initial analysis data to obtain target analysis data corresponding to the fault information; inputting the target analysis data into a pre-trained large model, and generating call chain atlas information and key index information corresponding to the fault information, the call chain atlas information is generated by the large model based on a tracking identifier of a call chain corresponding to each piece of initial analysis data and a stage identifier of each stage in the call chain; the key index information is obtained by performing performance analysis and anomaly detection on each stage in the call chain through the large model. According to the method, a large model technology is applied to the field of fault analysis and early warning, so that the stability and the operation and maintenance efficiency of the system are improved.

The invention belongs to the field of electrified rail transit overhead contact systems, and provides an overhead contact system diagnosis method and system based on a standard state, a storage medium and electronic equipment. The method comprises the following steps: constructing standard state data based on contact network detection parameters; obtaining any parameter data in the pantograph-catenary comprehensive detection period of the catenary, carrying out single parameter analysis based on the standard state data, and judging a stationary span and an abnormal span; performing multi-parameter association analysis on the abnormal span to obtain all abnormal parameter association data in the abnormal span; and substituting the abnormal parameter associated data into an interaction item regression model to obtain a dependent variable value of the interaction item regression model, and speculating the cause of the abnormality of the overhead line system. According to the method, diagnostic analysis and anomaly recognition of the parameters can be automatically completed, the possibility of manual intervention and misjudgment is reduced, and the operation and maintenance efficiency is improved.

The invention discloses a performance monitoring method, device and equipment of a photovoltaic power station and a storage medium, and relates to the technical field of solar energy industry, and the method comprises the steps: automatically obtaining the electrical data of each electrical device at a certain time interval, and carrying out the collection and unified management of all electrical data, thereby forming a traceable database; according to the photovoltaic power station electrical data management system and method based on the linear regression analysis, direct access of various subsequent analysis software and analysis algorithms through a data interface is facilitated, and subsequent access operations such as query and traceability are also facilitated, so that electrical data of a photovoltaic power station are comprehensively and effectively managed; the performance of each electrical device of the photovoltaic power station can be known through the linear regression model obtained through solving, and finally, the performance of the device can be known to be abnormal by screening out future generating capacity abnormal data through query operation. Advanced monitoring of electrical equipment of a photovoltaic power station is realized.

A transparent visual power grid operation and maintenance system comprises a data acquisition and preprocessing module which is composed of a sensor network and SCADA system docking unit and a data preprocessing unit; the data processing and analysis module is composed of a fault prediction and diagnosis unit, an operation and maintenance strategy optimization unit and a three-dimensional modeling and dynamic simulation unit; the visual display and user interaction module is composed of an AR / VR interface development unit and a user-defined interface and report generation unit; and the system integration and communication module is composed of a standardized API interface and cloud platform unit and a remote access and cooperation unit, and the scheme of the invention solves the problem that the complexity of power grid operation and maintenance system data causes that detection information is difficult to provide visually, thereby affecting the operation and maintenance work quality and efficiency.

The invention discloses a unified multi-version kernel service network traffic acquisition method and system based on a high-performance non-intrusive technology, and belongs to the technical field of computers. The method comprises the following steps: collecting traffic according to a kernel type of an operating system; and analyzing the collected flow data. Through the scheme of the invention, efficient collection of network traffic in various kernel versions and operation environments can be realized, and technical obstacles caused by kernel version differences do not need to be worried about. No matter a high-performance server, a device with a low-version Linux kernel or a container or a cloud environment with limited authority, seamless flow capture and analysis can be realized by using the system provided by the invention. Meanwhile, the protocol analysis and flow topology function helps the user to quickly understand the network condition and output a unified flow index, thereby greatly improving the efficiency of network operation, maintenance and analysis.

The invention relates to an intelligent video fault analysis and early warning method and device and computer equipment. The method comprises the following steps: acquiring an abnormal monitoring video of a power plant; performing fault analysis on the abnormal monitoring video, and identifying video abnormal data with an abnormal part in the abnormal monitoring video; performing scene perception analysis on the video abnormal data based on the real-time environment change information of the power plant to obtain scene perception analysis data; performing fault diffusion analysis on the abnormal condition of the power plant according to the scene perception analysis data and the video abnormal data to obtain fault diffusion path information; and performing early warning analysis on the video monitoring system of the power plant according to the scene perception analysis data and the fault diffusion path information to obtain equipment fault early warning data and equipment fault maintenance data of the video monitoring system. By adopting the method, prospective management of maintenance of the video monitoring equipment can be realized, and the operation and maintenance efficiency of fault analysis of the video monitoring equipment of the power plant is improved.