7097results about How to "Improve maintenance efficiency" patented technology

The invention provides a transformer life prediction method and system based on machine learning and the Internet of Things, and relates to the technical field of transformer life prediction, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of a transformer, constructing a multi-modal data fusion model based on a graph neural network, and carrying out the prediction of the life of the transformer; and mapping the dimensionality-reduced feature data into a graph structure, inputting transformer multi-modal operation data collected in real time into an optimized fuzzy inference system to obtain a health state evaluation result and a final residual life prediction result of the transformer, and obtaining a prediction result of the residual life of the transformer on the basis of transformer maintenance suggestions and historical maintenance records. Constructing a transformer digital twinborn model for maintenance strategy optimization, inputting the health state evaluation result, the residual life prediction result and the transformer maintenance suggestion into the transformer digital twinborn model, performing fault simulation and predictive maintenance analysis, and based on the fault simulation and predictive maintenance analysis result, obtaining a maintenance strategy of the transformer. And generating an intelligent maintenance scheme in combination with the expert knowledge base.

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 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 provides a photovoltaic station intelligent operation and maintenance simulation method and system based on digital twinning, and relates to the technical field of photovoltaic station operation and maintenance, and the method comprises the steps: constructing a digital twinning hierarchical structure through multi-modal detection signals through discrete wavelet packet transformation, tensor decomposition and a graph neural network, and determining structured data; extracting fault features by using a dynamic neural network, predicting a fault probability based on a Markov model, and triggering maintenance early warning; inputting the early warning signal into a maintenance strategy model based on multi-agent reinforcement learning, and generating a candidate maintenance time window set; the maintenance scheduling module optimizes sorting to obtain an optimal time window sequence, and the resource allocation module generates a personnel material allocation scheme by adopting a genetic algorithm; and iteratively optimizing the strategy parameters based on the maintenance cost, and outputting a final operation and maintenance implementation scheme.

The invention provides a hydropower station auxiliary machine equipment real-time monitoring method and system based on the Internet of Things, and relates to the technical field of information, and the method comprises the steps: constructing a pressure oil pump fault diagnosis model for a temperature and vibration related feature subset, and training a temperature and vibration feature sample of a preset fault type, establishing a mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; on the basis of the pressure oil pump fault diagnosis model, typical faults of bearing abrasion and poor gear meshing of the pressure oil pump are classified and recognized, and the temperature and vibration fault diagnosis result of the pressure oil pump is determined through the mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; and according to the fault diagnosis results of the temperature and vibration of the pressure oil pump, carrying out comprehensive decision making on a plurality of diagnosis results, according to the preset confidence of different diagnosis results, giving corresponding weight coefficients, and generating a final health state evaluation result of the pressure oil pump. The operation reliability and the maintenance efficiency of the pressure oil pump are improved.

The invention provides a semiconductor equipment fault prediction and maintenance method and system based on data fusion, and relates to the technical field of semiconductor equipment fault prediction and maintenance, and the method comprises the steps: collecting the data of a multi-source sensor, constructing a process-stress-material multi-dimensional state model, and calculating the failure probability of a key part. And when the failure probability exceeds a threshold value, a fault diagnosis process is triggered, fault mode identification is performed based on a digital twin model, and a fault development trend is predicted in combination with historical data. And finally, constructing a maintenance objective function, determining an optimal maintenance scheme by using an adaptive optimization algorithm and a maintenance decision rule base, and carrying out maintenance guidance. According to the method, the maintenance knowledge graph is constructed, so that continuous optimization of fault prediction and maintenance capability is realized, and the reliability and production efficiency of semiconductor equipment are improved.

The invention relates to the technical field of pipeline data processing and health state evaluation, and discloses a pipeline interior detection method and system based on multi-sensor fusion. The method comprises the following steps: performing health index calculation on pipeline state evaluation data to obtain a pipeline health state index, and performing risk prediction on the pipeline health state index to obtain a risk level; performing pipeline defect feature recognition to obtain defect feature data, and performing multi-category classification processing on the defect feature data to obtain defect type data; performing accurate position analysis on the defect type data to obtain defect space distribution data, performing structural integrity evaluation to obtain a defect severity quantitative data set, performing multi-criterion decision analysis on the defect severity quantitative data set and risk levels to obtain defect repair priority ranking data, and performing defect repair priority ranking on the defect repair priority ranking data; and performing intelligent repair scheme generation on the defect repair priority ranking data to obtain repair suggestion data. According to the invention, the state of the pipeline can be evaluated more comprehensively and accurately.

The invention relates to a real-time fault detection and automatic repair system for a power grid. The real-time fault detection and automatic repair system comprises a multi-level sensing module, a data processing module, an automatic repair module and a monitoring and logging module. The multi-level sensing module collects data such as voltage, current, frequency and temperature in operation of a power grid by deploying multiple types of sensors, and sends the data to the data processing module. And the data processing module analyzes the collected data through an embedded intelligent algorithm, identifies potential faults and generates fault diagnosis results. And the automatic repair module executes repair operations of isolating a fault area, reconstructing a power grid topological structure and recovering normal power supply according to a fault diagnosis result. The monitoring and log module records the operation state of the power grid and the fault diagnosis and repair process, and provides fault analysis and performance evaluation functions through a user interface. According to the system, real-time detection, accurate positioning and automatic repair of power grid faults and comprehensive monitoring of operation data are realized, and the safety and reliability of power grid operation are remarkably improved.

The invention discloses a fault detection method and device for secondary equipment of a transformer substation. The method comprises the following steps: acquiring real-time operation parameters of the secondary equipment of the transformer substation, inputting the real-time operation parameters into a pre-trained graph neural network model to obtain equipment operation characteristics, and determining an equipment state vector according to a plurality of equipment operation characteristics in a continuous time window and a message passing mechanism; determining an equipment health degree score according to the equipment state vector and a knowledge graph, and triggering the intelligent inspection robot to execute a deep detection task under the condition that the health degree is lower than a preset health degree threshold value so as to collect target test data; determining a target fault type according to the target test data, the knowledge graph and the inference rule base, obtaining a fault processing scheme corresponding to a historical fault case, and generating corresponding maintenance guide information according to the fault processing scheme, thereby achieving the real-time detection, precise fault diagnosis and automatic maintenance of the secondary equipment of the transformer substation. And the operation safety and the maintenance efficiency of the power system are improved.

The invention discloses an equipment fault information sharing system and method based on the Internet of Things, and relates to the technical field of equipment fault sharing. Causal relationships among parts, faults and operation parameters are identified, real-time operation data, historical fault data and maintenance records are integrated, association relationships among various types of data are combined, a fault knowledge graph is constructed, and fault information sharing is performed. Reasoning is executed through a rule engine in the knowledge graph, possible fault causes are deduced, and cross-modal fusion is performed on fault phenomena, parameter changes and historical records to obtain a more accurate fault cause reasoning result. According to the sharing system, after the parts, the operation parameters and the fault types of the electric water heater are associated, the comprehensive fault knowledge graph is constructed, so that when the electric water heater breaks down, the parts which possibly have abnormity can be displayed to maintenance personnel after reasoning, the maintenance efficiency is improved, and the maintenance cost is reduced. And the use safety of the electric water heater after maintenance is guaranteed.

The invention discloses a power transmission and transformation equipment fault early warning system based on online monitoring, and the system comprises the following modules: a multi-parameter sensor module which is used for collecting data in real time; the wireless sensor network module is used for transmitting the data to the data processing center for processing; the data fusion module is used for generating a comprehensive health score of the equipment by adopting a multi-modal data fusion algorithm based on a self-evolution nested network; the trend prediction module introduces an adaptive residual module to carry out trend prediction based on a nonlinear time sequence analysis technology of a generative adversarial adaptive residual network; the early warning threshold value adjusting module is used for dynamically adjusting an early warning threshold value by adopting a self-adaptive evolutionary algorithm of meta reinforcement learning; the maintenance suggestion generation module is used for generating maintenance suggestions; and the power grid dispatching linkage module is used for automatically linking a power grid dispatching system and detecting and adjusting an equipment load. According to the invention, a multi-parameter fusion and generative adversarial network method is adopted, and an adaptive residual module is introduced, so that accurate early warning of faults of power transmission and transformation equipment is realized.

The invention relates to the technical field of defect detection, and discloses a pipeline repairing method and system based on defect detection. The method comprises the following steps: performing acoustic detection on a pipeline based on a multi-frequency sound wave signal to obtain pipeline sound field data; carrying out electromagnetic wave scattering detection on the pipeline to obtain electromagnetic scattering data; constructing a sound field distribution diagram in the pipeline and an electromagnetic property distribution diagram on the surface of the pipeline; performing feature extraction on the sound field distribution diagram to obtain a first detection feature set, and performing feature extraction on the electromagnetic characteristic distribution diagram to obtain a second detection feature set; inputting the first detection feature set and the second detection feature set into a space-time diagram convolutional network model for pipeline defect diagnosis to obtain a defect position and a defect type; and performing causal reasoning and repair scheme matching on the pipeline according to the defect position and the defect type to obtain a target pipeline repair scheme, thereby improving the accuracy of pipeline defect detection, thereby improving the accuracy of pipeline repair.

The invention provides a power grid fault detection and positioning method and system for an intelligent power grid system. The method comprises the following steps: collecting a fault traveling wave signal when a power distribution line has a fault; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; carrying out traveling wave front edge detection on the target fault traveling wave signal, and determining the signal arrival time of the fault traveling wave signal; fault signal features are extracted from the target fault traveling wave signals through a frequency domain analysis method and a time domain analysis method; estimating the fault line characteristics of the fault line with the fault in combination with the fault signal characteristics and the signal arrival time; outputting the line type and the fault type of the fault line through a feature recognition model; selecting an optimal fault positioning algorithm by using a decision tree algorithm based on the line type and the fault type; and determining the line position of the fault line according to the target fault traveling wave signal by adopting an optimal fault positioning algorithm. The method has the effect of intelligently selecting the optimal positioning algorithm according to the line type.

The invention provides an intelligent operation and maintenance method and system for a photo-thermal power generation system based on the Internet of Things, and relates to the technical field of information technologies, and the method comprises the steps: obtaining temperature, humidity and vibration environment parameter data collected by a sensor disposed on power generation equipment; and receiving the environmental parameter data, comparing the environmental parameter data with a preset threshold value, and judging whether the environmental parameters exceed a normal range or not. According to the method, environmental parameters such as temperature, humidity and vibration of the equipment are collected in real time by arranging the sensors, when the parameters exceed the preset threshold value, performance parameter collection is triggered, weighted average calculation is carried out in combination with the environmental parameters, and the equipment health index is obtained; according to the method, intelligent assessment and predictive maintenance of the health state of the power generation equipment are realized, the operation reliability and the maintenance efficiency of the equipment are improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

The invention relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

The invention discloses a coal mine dust diffusion intelligent monitoring method based on a distributed sensing network, and relates to the technical field of coal mine dust diffusion intelligent monitoring, and the method comprises the following steps: deploying a plurality of sensing nodes in a coal mine, each node having a function of collecting environmental data, a distributed sensing network is formed through a wireless or wired communication module, and when all nodes are started, a unique node identifier is issued through a central controller, and self-inspection is carried out. Through bidirectional delay calculation and a progressive fine tuning strategy, high-precision time synchronization is achieved, and it is ensured that timestamps of data such as dust concentration and airflow are accurate and reliable. And data jump caused by large correction is avoided through gradual fine adjustment, data continuity is guaranteed, and the precision of dust diffusion analysis and an early warning model is improved. A redundant node management and automatic switching mechanism improves the fault processing capability of the system, avoids a monitoring blind area, accelerates maintenance response through fault information reporting, and ensures stable operation of a monitoring network.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

The invention discloses a concrete curing device for constructional engineering, and belongs to the technical field of constructional engineering. The device comprises the following modules: a data acquisition and sensing module for acquiring real-time parameters of the interior and surface of concrete through a temperature and humidity sensing network and a plurality of detection devices so as to support subsequent data analysis and decision; the internal temperature control execution module is used for controlling the internal temperature of the concrete through a snake-shaped cooling pipeline and precise flow regulation and maintaining the concrete within a proper curing temperature range; the surface curing execution module is used for continuously curing the concrete surface, and all-directional spraying of concrete surface curing moisture and cyclic utilization of water resources are achieved; the central control module is responsible for data analysis, maintenance decision optimization and execution instruction distribution, and intelligent control and remote monitoring of the maintenance process are achieved; and the performance evaluation module monitors the concrete strength development in real time, evaluates the maintenance effect, provides trend analysis and optimization suggestions, and provides a basis for continuous improvement of a maintenance decision.

The invention provides an RFID-based power supply management method for a tilt angle sensor of a power tower, and the method comprises the steps: employing a support vector machine and a decision tree, analyzing the historical operation data of the tower, building a tower fault prediction model, predicting the state of the tower through the tower fault prediction model, and obtaining the fault probability and time of the tower; according to a tower fault prediction result, a preventive maintenance plan is made, priority maintenance is arranged for the tower with the fault probability larger than a preset threshold value, and a power supply strategy is adjusted in advance for the tower predicted to have a fault, and load pressure is reduced; a tower group intelligent power supply management system is adopted, state information and a power supply strategy of a single tower are integrated into a whole power grid, the power supply load of each tower is dynamically adjusted through a load balancing algorithm, and for a tower in a dangerous state, the power supply load of the tower is reduced, and the load is transferred to a tower in a good state.

The invention discloses an explosion-proof junction box fault diagnosis method and system based on a sensor, which are used in the field of fault diagnosis, and the method comprises the following steps: obtaining historical multi-source sensor data reflecting the state of an explosion-proof junction box; extracting a time sequence sample of the preprocessed historical multi-source sensor data by using a sliding time window method; multi-domain features reflecting fault types are extracted from the time sequence samples, and a fault diagnosis model is constructed based on the feature combination with the optimal diagnosis performance; performing feature extraction on the multi-source sensor data acquired in real time, and inputting the extracted features into a fault diagnosis model for fault diagnosis; and the operation state and the diagnosis result of the explosion-proof junction box are displayed in real time. According to the method, the subtle change of the fault signal is captured from multiple angles, the Pearson correlation coefficient is used for screening the features significantly related to the fault type, and the dimension reduction processing is carried out through principal component analysis, so that the operation efficiency and accuracy of the model are improved.

The invention provides a rail transit platform door multilayer architecture intelligent operation and maintenance and predictive maintenance method and system, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: obtaining platform door operation parameters through a multilayer data collection system, and generating health state feature vectors; inputting the feature vectors into a deep neural network, and constructing a fault prediction model; based on the prediction result, a decision tree algorithm is adopted to evaluate the maintenance priority, and an intelligent maintenance scheme is generated; and pushing an instruction to maintenance personnel through the mobile terminal, and feeding back an optimization model by using new data to realize self-adaptive adjustment of a maintenance strategy.

The invention discloses a photovoltaic power generation efficiency evaluation method and device, equipment and a storage medium, and relates to the technical field of renewable energy sources, and the method comprises the steps: obtaining multi-source photovoltaic power generation data in real time, and the multi-source photovoltaic power generation data comprises electric meter data, panoramic image data, thermal imaging data, irradiance data and environment temperature data; performing image analysis on the panoramic image data and the thermal imaging data to obtain an image analysis result, and determining an abnormal photovoltaic panel according to an image recognition result; calculating the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result; acquiring historical power generation efficiency, and performing time sequence analysis according to the historical power generation efficiency and the real-time power generation efficiency to obtain an efficiency analysis result; and generating an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panel. According to the invention, the power generation efficiency of the photovoltaic power station can be accurately evaluated.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention relates to the technical field of industrial equipment management, and discloses an equipment management method for a 3D digital factory, and the method comprises the following steps: sensing the operation parameters of a network collection device through the Internet of Things; processing the operation parameters by utilizing a cloud computing or edge computing technology; dynamically generating an equipment health state based on the processed data; the equipment management system of the 3D digital factory comprises an Internet of Things sensing network used for collecting operation parameters of the equipment; the data processing module is used for carrying out cleaning, noise reduction, dynamic modeling and anomaly detection on equipment operation parameters; and the equipment health state generation module is used for generating the health state of the equipment. According to the invention, through the Internet of Things sensing network, dynamic modeling, intelligent scheduling and full-life-cycle management technology, the effects of equipment state real-time monitoring, accurate maintenance, energy efficiency optimization and comprehensive asset management are realized.

The invention relates to the technical field of power distribution station control, in particular to a reactive power optimization control system of a power distribution station. Comprising a power distribution station data acquisition module, a reactive prediction module, an optimization decision module, a distributed cooperative control module and a monitoring interaction module. The power distribution station data acquisition module acquires parameters such as voltage and current and guarantees data integrity; the reactive power prediction module performs modeling based on a graph neural network and generates a prediction report; the optimization decision module adopts a differential evolution algorithm to generate an optimal compensation scheme; the distributed cooperative control module realizes cooperation between devices through fractional calculus; and the monitoring interaction module realizes equipment diagnosis, aging prediction and dynamic feedback. Through an advanced algorithm and a cooperative control technology, high efficiency, reliability and intelligence of reactive power optimization control of the power distribution station are realized, and the operation efficiency and reliability of the power distribution station are improved.

The invention discloses a smart city street lamp intelligent control system and method. The method comprises the following steps: performing multi-aspect inspection on each hardware device through a comprehensive intelligent control module and a communication module; the environment monitoring module is used for collecting multi-source data in real time and integrating and analyzing the multi-source data through the data fusion module; generating a dynamic dimming strategy of the common road section and the high-priority road section according to the collected data and regional requirements; brightness adjustment and on-off control are executed one by one through the single lamp control module, and equipment operation parameters are collected through the real-time feedback and monitoring module. The remote management platform is used for monitoring the running state of the street lamp in real time and dynamically adjusting strategy optimization analysis; different operation scenes are simulated through a system simulation test, actual deployment is carried out in a pilot area, and parameters and operation logic are optimized. Through modular design, intelligent decision making and data driving modes, the management efficiency and the energy-saving level of urban street lamps are remarkably improved, and reliable technical support is provided for smart city construction.

The invention discloses a distribution network multi-parameter data fusion fault positioning method and system, and the method and system achieve the omnibearing monitoring of a power distribution network fault through the collection of multiple types of data, such as electrical signals, video images, meteorological information and environmental parameters. Compared with a traditional fault positioning method of a single information source, the system can reflect the fault condition more truly, and the positioning accuracy and reliability are remarkably improved. Machine learning or a deep learning model is utilized to analyze fusion data, fault types and positions are automatically identified, intelligent diagnosis is realized, manual intervention is reduced, the operation and maintenance cost is reduced, and the processing efficiency and accuracy are improved. The real-time data acquisition and transmission technology guarantees the system to monitor in real time and rapidly alarm potential faults. According to the method, historical data and an expert knowledge base are combined, fault causes are deeply analyzed, accurate information and maintenance suggestions are provided for maintenance personnel, operation and maintenance decisions are optimized, and the maintenance efficiency and quality are enhanced. The system provides an efficient and intelligent solution for distribution network fault positioning.