1695results about How to "Improve operation and maintenance efficiency" patented technology

The invention provides a power distribution network automatic operation and maintenance method and system based on artificial intelligence, and relates to the technical field of power operation and maintenance, and the method comprises the steps: collecting the operation data of a power distribution network in real time through a distributed sensor network, transmitting the collected operation data to a cloud data center, and designing a multi-agent reinforcement learning framework, a power distribution network is divided into a plurality of sub-regions, and an intelligent operation and maintenance agent makes a personalized fault processing scheme by monitoring and analyzing a digital twin model in real time, and makes an optimal execution plan based on the fault processing scheme in combination with real-time road condition information and the position of maintenance personnel; and issuing the optimal execution plan to the automation equipment and a mobile terminal of a maintainer through a security encryption channel, extracting preventive maintenance rules from historical operation and maintenance records, and making a preventive maintenance plan based on the extracted preventive maintenance rules in combination with a pre-trained equipment life prediction model.

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 an intelligent maintenance decision-making method and system for a numerical control machine tool based on digital twinning, and relates to the technical field of equipment maintenance, and the method comprises the steps: obtaining the structural data and simulation precision requirements of the numerical control machine tool, determining the optimal grid size and distribution parameters of a finite element model, obtaining operation conditions, and adding the operation conditions to a multi-physics coupling modeling module, generating a machine tool digital twinborn model, fusing the machine tool digital twinborn model with the physical field, generating a machine tool operation degradation model, and optimizing to obtain a self-evolution digital twinborn model; obtaining machine tool operation parameters, performing preprocessing to obtain multi-modal perception data, extracting multi-view potential features, determining potential correlation features, and obtaining a machine tool health state evaluation result; the method comprises the following steps: generating a multi-level fault prediction result, determining a candidate maintenance scheme, adding the candidate maintenance scheme and the multi-level fault prediction result into a knowledge graph construction module, generating a semantic association network, and solving to obtain an optimal maintenance decision in combination with a multi-objective optimization algorithm.

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention provides a distributed interface defect detection method and system for a micro-service architecture, and relates to the technical field of micro-services, and the method comprises the steps: collecting interface calling data through deploying a distributed tracking probe, and constructing a service calling topological graph; inputting the feature sequence matrix into a long-short term memory neural network to extract a time sequence dependence feature, calculating an attention weight coefficient between nodes in combination with a graph attention network, and fusing node features to generate a state vector; calculating a fault situation vector of the service node based on the fault propagation prediction model, constructing a fault propagation path diagram, and carrying out fault source positioning and early warning; and dynamic scheduling of the micro-service nodes is realized by dynamically calibrating the early warning threshold and continuously optimizing the prediction model. According to the method, the fault prediction accuracy is improved, the fault propagation risk is effectively reduced, and the system reliability is enhanced.

The invention provides a photovoltaic module temperature monitoring method and system, and relates to the technical field of temperature monitoring, and the method comprises the steps: obtaining the standardized temperature data of a photovoltaic module through infrared thermal imaging and visible light image collection in combination with a space registration algorithm and edge calculation. And a temperature prediction model is constructed by using wavelet transform and a graph convolutional neural network, and recognition of a temperature anomaly region is realized. And performing fault analysis by adopting hierarchical clustering and a graph neural network, generating early warning information including fault positioning, early warning levels, fault types and processing guidance, and sending the early warning information to an operation and maintenance management platform, thereby realizing intelligent temperature monitoring and fault early warning of the photovoltaic module.

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 device access method and system based on protocol conversion, and relates to the technical field of dynamic information processing of multi-device access, and the method comprises the following steps: 1, connecting a device to be accessed through an adaptive physical interface, and receiving an original communication protocol sent by the device according to the physical interface standards of different devices; according to the device access method and system based on protocol conversion, all access devices are managed in a centralized mode through a unified standard protocol, the management efficiency of the system is greatly improved, communication resources can be reasonably distributed, communication delay or data loss caused by resource contention of some devices is prevented, and the user experience is improved. The communication link and the data transmission strategy of each device are dynamically adjusted, it is ensured that the system can still operate stably under the high-load condition, the communication encryption unit is responsible for encrypting communication data between the devices and the system, the data are prevented from being stolen or tampered in the transmission process, and the integrity of the data and the credibility of the source of the data are ensured.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

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 technical field of industrial automation, and discloses a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis, which comprises the following steps of: 1, acquiring operation data of the industrial control equipment through at least one industrial control protocol which is a Modbus protocol, an OPC UA protocol or a PROFIBUS protocol, the operation data comprises sensor data, register data and equipment state data of the equipment; and 2, analyzing the collected data packet by using a protocol analyzer, extracting key information related to the equipment operation state, the key information comprising parameters such as equipment working frequency, load, vibration, temperature and pressure, and converting the key information into a uniform data format. By collecting, analyzing and analyzing the operation data of the industrial control equipment in real time, abnormal conditions in operation of the equipment can be found in time, spreading of equipment faults is avoided, and stable operation of the equipment is guaranteed.

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 relates to an intelligent monitoring method and system for an air environment monitoring station, and belongs to the technical field of environment monitoring. The method comprises the following steps: acquiring multi-modal monitoring data, performing data association analysis on the multi-modal monitoring data by adopting a multi-source data association model, and establishing an association relationship among the multi-modal monitoring data; performing data exception monitoring on the multi-modal monitoring data, and triggering and starting an exception type diagnosis event when data exception is monitored; extracting a first data subset related to an exception type from the multi-modal monitoring data; extracting a second data subset associated with the first data subset from multi-modal monitoring data; and performing fault diagnosis based on the first data subset and the second data subset, and outputting an abnormality diagnosis result. According to the method, the multi-source data association model is utilized, the association between different types of data can be effectively analyzed, detailed fault analysis and diagnosis are carried out, the fault response time is shortened, and the operation and maintenance efficiency is improved.

The invention provides a modular power supply system configuration method and system for a micro-module machine room, and relates to the technical field of electric power, and the method comprises the steps: collecting power utilization state data and load distribution data of functional equipment in the micro-module machine room, building a power supply network topology model, dividing a power supply system into a first-stage load, a second-stage load and a third-stage load, an independent power supply loop and a data acquisition unit are respectively configured in the first-stage load, the second-stage load and the third-stage load; a power supply network topology model is updated according to the power utilization data acquired by the data acquisition unit, a reinforcement learning algorithm is adopted to generate an adaptive power distribution strategy, and a modular energy storage unit is arranged to provide a standby power supply for a primary load and a secondary load; and constructing a machine learning fault prediction model based on the power consumption data, and dynamically adjusting a power supply path and a power distribution scheme of a power supply loop.

The invention discloses a digital GIS equipment fault intelligent monitoring management system, and relates to the technical field of fault intelligent monitoring, and the system comprises an information retrieval module which takes attribute information as a constraint, and carries out the networking retrieval to obtain a sample fault event set; the event clustering module is used for obtaining a plurality of high-frequency fault event sets; the traceability analysis module is used for determining a high-frequency fault factor set and generating a high-frequency fault feature matrix; the fixed-point monitoring module is used for obtaining a real-time monitoring data set and constructing a monitoring data matrix; the similarity analysis module is used for performing similarity analysis on the high-frequency fault feature matrix and the monitoring data matrix to determine comprehensive similarity; and the fault early warning module is used for performing fault early warning on the target equipment. Through the method and the device, the technical problem that real-time and accurate monitoring and management are difficult due to the fact that the intelligent degree and the detection accuracy are insufficient in the prior art can be solved, and the technical effect of improving the automation degree and the detection accuracy is achieved.

The invention discloses an underground operation and maintenance method and system based on big data analysis, and relates to the technical field of coal mine underground operation and maintenance. The underground operation and maintenance method based on big data analysis comprises the following steps: evaluating the stability performance of a sensor; evaluating signal fluctuation; evaluating sensor interference; and operation and maintenance feedback. According to the method, the sensor stability performance evaluation value is obtained through underground sensor monitoring data, whether stability adjustment is carried out or not is judged, then the signal fluctuation evaluation value is obtained by combining the signal fluctuation data and the sensor stability performance evaluation value subjected to stability adjustment, and whether signal fluctuation adjustment is carried out or not is judged. A sensor interference evaluation value is obtained by combining the signal fluctuation evaluation value obtained after signal fluctuation adjustment and sensor interference data, whether sensor interference adjustment is carried out or not is judged, and finally operation and maintenance feedback is carried out, so that the effect of improving the underground operation and maintenance efficiency is achieved. The problem of low underground operation and maintenance efficiency based on underground data in the prior art is solved.

The invention discloses a power distribution area equipment monitoring intelligent analysis method, and relates to the technical field of power system equipment monitoring, and the method comprises the steps: collecting and preprocessing the real-time operation data of power distribution area equipment; performing feature extraction on the real-time operation data by using a deep learning model to obtain an operation state feature vector; performing similarity calculation on the running state feature vector and a normal running state feature vector in a historical database to obtain a preliminary abnormal degree quantitative index; performing equipment state evaluation and classification according to the preliminary abnormal degree quantitative index to obtain an equipment state evaluation result; and taking corresponding measures according to an equipment state evaluation result, automatically generating a fault diagnosis report and pushing the fault diagnosis report to a maintenance terminal. The multi-parameter monitoring and deep learning feature extraction technology adopted by the invention improves the accuracy and comprehensiveness of anomaly detection, and can identify some potential fault features which are difficult to find by a traditional method.

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 relates to the technical field of monitoring or controlling a charging station, in particular to a charging pile safety monitoring method and system, and the method comprises the following steps: step 1, monitoring: step 1.1, electric leakage monitoring: installing a high-sensitivity electric leakage sensor in a charging pile and a charging gun line, continuously monitoring the electric leakage current in real time, and monitoring the electric leakage current in real time; the monitoring frequency is that data are collected once every 10 milliseconds; step 1.2, overload and short circuit monitoring: real-time current change of a charging circuit is monitored by using a current transformer, and the acquisition frequency is once every 5 milliseconds; step 1.3, monitoring the temperature; according to the scheme, through monitoring of electric leakage, overload and short circuit, temperature, smog and flame, charging gun safety, vision and the like in combination with intelligent analysis, the operation state and the surrounding environment condition of the charging pile can be comprehensively, timely and accurately mastered, electrical faults, temperature anomalies and potential safety hazards can be rapidly detected and judged, and the safety and reliability of the charging pile are improved. And all-around real-time monitoring of the potential safety hazard of the charging pile is realized.

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 an intelligent campus big data acquisition and management system based on cloud computing, and relates to the technical field of intelligent campus construction and big data processing, and the system comprises the following components: a big data acquisition module, a cloud computing resource pool, a dynamic resource scheduling engine, an intelligent load balancer, and a visual monitoring and management platform. The system realizes real-time and comprehensive acquisition of multi-source heterogeneous data in the campus through the big data acquisition module by using the Internet of Things (IoT) technology, a sensor network and an API interface, can effectively remove repeated, wrong and missing data through the data cleaning and preprocessing process, improves the data quality, and in addition, improves the data processing efficiency. The visual monitoring and management platform provides a visual monitoring interface, and supports an administrator to perform remote configuration, task scheduling and resource optimization operation, so that the efficiency of data management and operation and maintenance is greatly improved.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention discloses a multi-dimensional comprehensive online monitoring and fault early warning platform for a main transformer of a gas turbine power plant, and relates to the technical field of power transformer monitoring. In order to solve the problems that in the prior art, the accuracy of a monitoring result highly depends on the quality of collected data and the reliability of a sensor, wrong early warning is possibly caused when the sensor breaks down or data is inaccurate, specific adjustment and calibration cannot be carried out aiming at different transformers and environmental conditions, and the environmental adaptability is low. Multi-dimensional monitoring is carried out on the main transformer based on the sensor assembly, the load of data transmission is reduced through edge side processing, integrity verification is carried out on received dynamic monitoring data, meanwhile, the real-time performance and integrity of the data are ensured, and the transformer health degree evaluation model carries out comprehensive evaluation on the operation state of the main transformer. The health state and the fault type of the main transformer can be accurately judged, and timely and accurate fault information is provided for operation and maintenance personnel.

The invention discloses a distribution network line fault early warning and accurate positioning system, and relates to the technical field of distribution network line monitoring. In order to solve the problems that in actual operation, due to the complexity of a power grid, the situation of false alarm or missing alarm may exist, and in actual application, accurate fault positioning is crucial for quickly recovering power supply and reducing power failure time, and the accuracy of fault position positioning is low. Distribution network line data and traveling wave signals are accurately acquired in real time through the data acquisition unit, abnormal changes in a distribution network can be found in time through the fault early warning unit, the fault type can be rapidly identified, the fault occurrence position can be accurately calculated through the fault positioning unit, the rod fixing data of a fault point can be determined, and the fault positioning accuracy can be improved. According to the invention, the time of troubleshooting by operation and maintenance personnel is reduced, the fault processing efficiency is improved, the operation and maintenance personnel can remotely monitor the operation state of the power distribution network, the convenience and efficiency of operation and maintenance are improved, intelligent monitoring and management of the power distribution network are realized, and the operation and maintenance cost is reduced.

The invention discloses a dynamic traffic scheduling method and system based on an SD-WAN technology, which can timely discover potential problems in a network by monitoring performance parameters of network links and nodes in real time, and can effectively relieve network congestion and improve the overall network performance based on analysis of the performance parameters and dynamic adjustment of network routing and bandwidth allocation strategies. According to the method, a high-flow time period, a low-flow time period and key service flow are identified, so that the distribution of network resources is more reasonable and efficient, the adjusted network performance is evaluated in real time, the expected effect of each adjustment can be ensured, and if the performance does not reach a preset standard, the scheduling strategy is continuously optimized, and the scheduling efficiency is improved. According to the technical scheme, the method is beneficial to adapting to rapidly changing network environments and service requirements, the stability and reliability of the network are enhanced, the network states, scheduling strategies and performance evaluation results before and after adjustment are recorded in logs, and operation and maintenance personnel can rapidly position network problems by analyzing log data, so that the operation and maintenance efficiency and accuracy are improved.

The invention relates to the technical field of signal processing, in particular to an unmanned aerial vehicle self-noise filtering method and system based on multichannel time-frequency spatial filtering. The method comprises the following steps: acquiring a multi-channel noise-containing sound signal by using a microphone array carried by an unmanned aerial vehicle, and performing short-time Fourier transform to obtain a frequency domain signal; estimating a ratio mask by using a deep neural network, and performing logarithm normalization processing on the frequency domain signal; remodeling the sound signal to match the FFT length of the time-frequency spatial filter, and constructing the time-frequency spatial filter in combination with the multi-channel ratio mask; removing the self-noise of the unmanned aerial vehicle by using the filter and obtaining a target sound signal; and finally, converting the frequency domain signal into a time domain signal through inverse short-time Fourier transform. Through innovative fusion of multi-channel acquisition, deep learning and time-frequency space filtering, the self-noise of the unmanned aerial vehicle in a complex environment is accurately filtered, and the accuracy and efficiency of transformer substation noise monitoring are remarkably improved. Reliable acoustic data are provided for state monitoring of the power equipment, and the method has important engineering application value.

The invention is suitable for the field of electric power safety, and provides a fault prediction and maintenance method and system for safe operation of electric power equipment, and the system comprises a data collection module, a data analysis and abnormity identification module, a dynamic alarm and response module, a geographic information system integration module, and a maintenance path optimization module. The system adopts a non-contact dynamic electric field monitoring system to monitor power equipment, can identify potential insulation failures or electrical faults in real time, and significantly reduces the risk of contact between operation and maintenance personnel and high-voltage equipment. And in combination with an intelligent alarm system, real-time data are analyzed through machine learning, an alarm is automatically triggered, and the accuracy and response speed of fault identification are improved. In addition, the monitoring system is integrated with a geographic information system, automatic optimization of an optimal maintenance response path is achieved, and it is ensured that faults are rapidly processed. According to the comprehensive method, the safety of the power equipment is improved, the fault risk is reduced, the maintenance efficiency is optimized, and the comprehensive method has important practical value.

The invention relates to an intelligent fault prediction system based on deep learning, and aims to improve the fault prediction capability and operation and maintenance efficiency of an IT system. The system comprises a data collection module, a preprocessing module, a deep learning model module, a fault prediction module and an alarm feedback module. The system collects data, performs cleaning, normalization and feature extraction on the data, and performs training and prediction by using deep learning algorithms such as a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The core technology is to analyze operation data in real time by using a deep learning model and generate a fault prediction result and early warning information. The fault prediction module analyzes data in real time, and the alarm and feedback module provides feedback for system optimization. According to the system, the accuracy and the real-time performance of fault prediction are remarkably improved, manual intervention is reduced, the maintenance cost is reduced, and the system stability and the operation and maintenance efficiency are improved. The method is widely applicable to the fields of data centers, network equipment and the like, and has remarkable technical and commercial values.

The invention belongs to the technical field of operation and maintenance database management, and discloses an operation and maintenance information database intelligent establishment management system, which comprises a monitoring area division module, an operation and maintenance data acquisition module, an operation and maintenance frequency correction judgment module, a correction operation and maintenance frequency analysis module, an operation and maintenance quality anomaly analysis module, an operation and maintenance quality anomaly judgment module and a data storage library. When the power operation and maintenance data is acquired, the personnel operation and maintenance data, the equipment operation and maintenance data and the correction influence data are acquired, so that a more comprehensive data analysis basis can be provided, and the accuracy and the reliability of the data are ensured. When the operation and maintenance frequency analysis is carried out, the analysis of the corrected operation and maintenance frequency is further carried out based on the judgment result of the operation and maintenance frequency correction demand analysis, and the operation and maintenance condition and demand of the power system are truly reflected. According to the method, the abnormal monitoring area and the abnormal operation and maintenance personnel are further identified during abnormal operation and maintenance condition analysis, so that the operation and maintenance work is more targeted.

The invention discloses an ash removal monitoring method, device and system for a bag type dust collector and a medium. The method comprises the following steps: acquiring lifting valve time sequence data, pulse valve time sequence data and bag type dust collector system pressure difference of each box chamber of the bag type dust collector in an offline ash removal mode; based on the time sequence data of the lifting valve and the time sequence data of the pulse valve, determining an injection period, selecting differential pressure data of a bag type dust collector system in the injection period, and performing preprocessing; performing correlation analysis on the preprocessed differential pressure of the bag type dust collector system and the time sequence data of the lifting valve to obtain a change rule of the differential pressure of the bag type dust collector system along with the working of the lifting valve under a normal dust cleaning working condition; and based on the change rule, judging the dust removal effectiveness of the current box chamber. According to the method, the ash removal effectiveness of each box chamber of the bag type dust collector can be accurately monitored, so that which box chamber is abnormal in ash removal can be accurately monitored, the fault box chamber can be quickly and accurately found, and the operation and maintenance efficiency of the bag type dust collector is improved.