356results about How to "Reduce troubleshooting time" patented technology

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 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 discloses a photovoltaic power station management method and system based on artificial intelligence and big data analysis, and relates to the technical field of photovoltaic power generation. The photovoltaic power generation group comprises a plurality of photovoltaic group strings used for connecting a plurality of photovoltaic panels, at least one combiner box, a direct current cable, a central inverter and an alternating current cable; according to the method, the running state of a photovoltaic power generation set is monitored in real time, abnormal conditions are rapidly recognized based on comparison of predicted power generation capacity and first photovoltaic power generation capacity, faults are subjected to hierarchical management through a comprehensive fault evaluation index, a proper rectification scheme is matched, in addition, the state of a photovoltaic panel is subjected to nondestructive testing through visible light and infrared imaging technologies, and the reliability of the photovoltaic power generation set is improved. According to the method, meteorological data and historical operation parameters are combined, fault reasons and damage degrees are comprehensively evaluated, fault data are updated and uploaded to a photovoltaic data platform after maintenance is finished, model performance is optimized, and the management method improves response speed, reduces fault processing time and reduces maintenance and management cost.

The invention relates to a data channel state sensing method based on multi-source time sequence data fusion and a topological structure. The method comprises the following steps: collecting multi-source time sequence data; cleaning is conducted; obtaining a multi-source fusion time sequence; constructing a knowledge graph structure, wherein the knowledge graph structure comprises data and operation conditions of an equipment link topological structure, an equipment body and topological association equipment; obtaining a trained long short-term memory network model; and inputting real-time data into the trained long-short-term memory network model, predicting the real-time data by the trained model, and dynamically sensing the running state of the equipment. According to the method, a knowledge graph structure is adopted, modeling is carried out on data and operation conditions of an equipment link topological structure, an equipment body and associated equipment, the complex association relation between the equipment can be expressed in the mode, the sensing capacity of the equipment state is improved in combination with the actual operation data, and the real-time performance of the equipment is improved. Therefore, the monitoring system can perform more comprehensive state analysis by using the existing professional knowledge and the association between the devices.

The invention discloses a source-grid-load-storage integrated micro-grid intelligent scheduling system, and relates to the technical field of micro-grid management, the system comprises a comprehensive scheduling platform, the comprehensive scheduling platform is in communication connection with an energy data acquisition module, an energy prediction module, an intelligent scheduling module, an energy storage management module and a fault early warning module, and the modules are in electric signal connection. By integrating the sensor network and the data acquisition and processing technology, the running states of various energy devices and energy storage devices in the micro-grid are monitored in real time, the reliability and stability of the system are remarkably improved, potential device faults can be found and early warned in time, fault enlargement is avoided, and the reliability and stability of the system are improved. The power failure time and economic loss caused by faults are reduced, the charging and discharging strategy of the energy storage equipment can be flexibly adjusted according to the real-time load requirement and the renewable energy power generation condition, it is ensured that the microgrid can keep stable electric energy supply under various working conditions, and the power utilization requirement of a user is met.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention discloses a multistage safety response control method of an industrial and commercial liquid cooling energy storage system, and relates to the technical field of electric energy storage systems, a sensor network collects an operation state data time sequence matrix, and abnormal data cleaning is carried out on the time sequence matrix; the method comprises the following steps: preprocessing a vibration signal of a cooling system, extracting characteristic data of abnormal vibration through Fourier transform and energy distribution analysis, and positioning a time-frequency distribution abnormal region; generating a time sequence multivariable data set according to the characteristic data of the abnormal vibration, determining a cause label according to the fused time sequence multivariable data set, obtaining the inherent frequency and the resonant frequency of the cooling system in combination with simulation analysis, and matching and determining the cause of the abnormal vibration; an LSTW neural network is used for predicting the abnormal resonance risk, and corresponding countermeasures are taken for the cooling network according to the prediction result; unnecessary vibration caused by error adjustment is avoided, the relation between cooling efficiency and safety is balanced, and unnecessary power loss is reduced.

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

The invention provides a power distribution automation terminal debugging system and method based on an IEC104 protocol, and the method comprises the steps: inputting an IP address, a port number and a channel address of a device as parameters after a manufacturer selection module selects a manufacturer name, transmitting IEC104 message data information through a message communication module, a message sending module and a message receiving module, and transmitting the IEC104 message data information to a power distribution automation terminal. An IEC104 message is analyzed through a man-machine interaction interface, the original message and the message meaning represented by each byte of each original message are displayed through the man-machine interaction interface, finally, an information value in the analyzed IEC104 message corresponds to a substation point table, and real-time feedback and display of data are achieved. And meanwhile, a debugging log is recorded. And the method is adaptive to a plurality of manufacturers, so that the same software is used to debug terminal equipment of different manufacturers. The problem that multiple sets of software are used when a user debugs multiple manufacturer devices is solved, and the daily working efficiency is improved.

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 discloses a transformer on-line monitoring and fault early warning system, and particularly relates to the technical field of power equipment monitoring, and the system comprises a signal collection module, a signal filtering module, a broadband focusing module, a signal complexity analysis module, a threshold self-tuning module, a fault early warning module and a fault positioning module. The signal acquisition module acquires operation data of a transformer in real time through a sensor network, the filtering module removes noise, and the broadband focusing module converts broadband signals into narrow-band signals, so that the resolution is improved; the signal complexity analysis module evaluates the signal complexity, and the threshold value self-tuning module dynamically adjusts the threshold value; the fault early warning module judges whether to trigger early warning based on the adjusted threshold value, and the fault positioning module determines the position of the partial discharge source through a cross positioning algorithm; according to the invention, the problem that the accuracy of early warning is affected due to the fact that a traditional fixed-threshold monitoring method is easy to generate false alarm or missing alarm can be solved, and operation and maintenance personnel can be helped to quickly determine the position of a fault source through cross positioning.

The invention relates to the field of hybrid power transmission line fault maintenance, in particular to a sectional type multi-sensor detection hybrid power transmission line fault rapid positioning and diagnosing system, which comprises the following modules: a distributed fault positioning module, a fault diagnosis module, a fault diagnosis module and a fault diagnosis module, rapidly positioning a fault point according to the instantaneous traveling wave signal of the line by adopting a traveling wave measurement technology; and the data acquisition and transmission module is used for summarizing the line instantaneous traveling wave signal data acquired by each sensor and transmitting the data to the intelligent data analysis platform. Through the distributed fault positioning module, a fault point can be quickly positioned in an extremely short time, the fault searching time is greatly shortened, the emergency response capability of a power grid is improved, the overall coverage of the whole hybrid power transmission line is ensured through the installation mode of the distributed sensor, and no matter which position the fault occurs, the fault point can be quickly positioned. And fault signals can be captured and processed in time.

The invention relates to the technical field of bus joint temperature measurement, and discloses a method for automatically monitoring the temperature of each phase busbar for infrared temperature measurement of a bus joint, and the method comprises the steps: collecting the surface temperature data and real-time images of each phase busbar of the bus joint in real time through installing an infrared temperature measurement sensor and image collection equipment. After preprocessing, temperature data is converted into an image form, and the image form and a bus bar real-time image are superposed to generate a fusion image, so that the actual form and the temperature distribution condition of the bus bar can be observed at the same time. Through presetting a high temperature early warning threshold value and a temperature gradient change threshold value, a temperature gradient direction is calculated and a change trend is analyzed so that prediction and early warning of temperature abnormity are realized. And when the temperature or temperature gradient change exceeds a preset threshold value, an early warning mechanism is triggered, an alarm signal is sent out, and an abnormal position and related information are displayed through a visual interface. According to the invention, the use safety and reliability of the bus duct are improved, and the fault risk caused by abnormal temperature is reduced.

The invention provides an overhead transmission line fault positioning method based on double-end induction traveling waves. The method comprises the following steps: extracting non-fault phase induction traveling waves of a transmission line; judging the wave head of the non-fault phase induction traveling wave and determining the starting moment of the wave head; calculating traveling wave head time difference on two sides of the fault point based on the wave head starting time; the traveling wave propagation speed is calculated based on the line distance between the two adjacent traveling wave acquisition devices and the traveling wave head time difference acquired by the two adjacent traveling wave acquisition devices, and the position of the fault point is determined based on the traveling wave propagation speed, the traveling wave head time difference on the two sides of the fault point and the distance between the traveling wave acquisition devices on the two sides of the fault point. Compared with a traditional method, the method can accurately calculate the fault position of the power transmission line under the conditions that a fault phase traveling wave acquisition device is damaged and fault traveling waves are missed in acquisition, reduces the troubleshooting time, and improves the operation and maintenance level of the line and the power supply reliability.

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a transformer operation inspection-oriented causal relationship enhanced knowledge graph construction method and system. According to the method, information in a transformer fault processing text is deeply mined, a natural language processing technology and a deep learning algorithm are utilized, and accurate recognition and relation extraction of key entities such as fault description, reasons, phenomena, detection methods, detection results and operation and maintenance suggestions are achieved. Structured knowledge is organized and stored in a graph database, and a comprehensive, deep and easy-to-query transformer fault operation and maintenance knowledge graph is formed. By introducing a causal scientific theory, the causal relationship in the knowledge graph is remarkably enhanced, fault operation and maintenance causal knowledge with a more stable causal effect is formed, the accuracy and robustness of the causal relationship in the knowledge graph are improved, and operation and maintenance personnel can more intuitively understand complex association between fault phenomena and reasons.

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

A drainage equipment fault tracing method in combination with a knowledge graph relates to the technical field of drainage equipment fault intelligent analysis, and mainly comprises the following steps: step 1, carrying out data acquisition and preprocessing; step 2, performing knowledge graph construction; step 3, fault feature extraction is carried out; step 4, performing fault diagnosis and traceability; and step 5, result display and feedback are carried out. According to the method, rapid and accurate traceability of the drainage equipment fault is realized by combining a knowledge graph technology. A fault source can be quickly positioned, the maintenance efficiency is improved, the downtime of equipment is reduced, and the influence on the operation of a drainage system is reduced.

The invention provides a poor-perception-quality root cause analysis method, a poor-perception-quality root cause analysis device, a computer storage medium and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring multi-modal data of a target object; a hypergraph structure for the target object is constructed according to the multi-modal data, the hypergraph structure comprises a plurality of initial nodes and a plurality of hyperedges used for connecting the initial nodes, and the initial nodes are used for representing a plurality of feature vectors obtained after feature extraction is carried out on the multi-modal data; the plurality of hyperedges are used for representing association relationships among the plurality of feature vectors respectively; and inputting the hypergraph structure of the target object into a pre-trained hypergraph neural network model to obtain a root cause analysis result of poor perceptual quality for the target object. The application range of root cause analysis on various types of environments can be widened, and the accuracy of root cause analysis results can be improved.

The invention discloses a transformer area topology identification method and system based on Kirchhoff's law, and relates to the technical field of low-voltage transformer area topology identification, and the method comprises the steps: obtaining low-voltage rural power grid transformer area LMUs and user meter data, and carrying out the preprocessing; for LMUs data, establishing an LMUs-LMU identification relation model based on the Kirchhoff's law; based on the sequence of the LMUs from high to low, establishing an LMU-LMU relation model based on the Kirchhoff's law for the LMUs to be compared and the LMUs in the existing LMU-LMU topological structure one by one, and obtaining the real positions of the LMUs to be compared in the LMU-LMU structure; establishing an LMU-user meter identification relation model; and based on the identified complete LMU-LMU structure, establishing an LMU-household meter identification model based on Kirchhoff's law for each household meter and all LMUs, and finally determining the LMU to which the current household meter belongs. According to the invention, in a power topology identification scene based on data analysis, the investment of resources such as manpower and material resources is reduced, the topology identification accuracy can be ensured, and a set of transformer area topology identification system with low cost and high efficiency is formed.

The invention belongs to the technical field of power fault diagnosis, and particularly relates to a line fault diagnosis method based on satellites and meteorology, which comprises the following steps: regularly acquiring satellite images and storing the satellite images into an image database, identifying risk source information and storing the risk source information into a hidden danger database; starting a pre-disaster satellite emergency patrol process of the disaster area, bringing an acquisition result into an image database, identifying risk source information, calling data in a hidden danger database for comparison, and outputting a risk source list of the disaster area; the method comprises the following steps: starting an emergency patrol process after a fault of a fault area, bringing an acquisition result into an image database, identifying risk source information, calling data in a hidden danger library for comparison, outputting risk source change information before and after the fault according to a comparison result, and determining the risk area according to the risk source change information, meteorological data during the fault and fault characteristics of a fault site. And fault source screening is carried out. According to the invention, by using real-time data provided by satellites and meteorology and combining an intelligent analysis technology, rapid line channel foreign matter source searching before a disaster and accurate source tracing after a fault are realized.

The utility model provides a processing device for cutting a main insulating layer of a cable joint. The processing device comprises a clamping mechanism and a feeding mechanism, the clamping mechanism comprises a clamping moving unit and two clamping jaws, and the two clamping jaws are movably arranged on the clamping moving unit; the feeding mechanism is arranged on one side of the clamping mechanism and comprises a rotating unit, a feeding moving unit and a cutting tool, the cutting tool is assembled at the output end of the rotating unit, and the rotating unit is movably arranged on the feeding moving unit. According to the utility model, the manufacturing efficiency can be greatly improved, the fault repair time can be reduced, the development productivity can be liberated, the fault rate of the cable joint in the cable operation can be reduced, the unnecessary economic loss can be reduced, and the rapid development of the power industry can be promoted.

The invention relates to a snowflake-type power distribution network self-healing processing logic optimization method, system and device and a medium, and belongs to the technical field of power system automation, and the method comprises the following steps: constructing a protection cooperation rule of snowflake-type power distribution network self-healing logic and a low-voltage photovoltaic grid-connected circuit breaker; optimizing the centralized logic of the master station; and constructing a distributed rapid self-healing logic based on the protection cooperation rule and the optimized master station centralized logic so as to optimize the self-healing processing logic of the snowflake type power distribution network. According to the invention, by introducing the direction signal and the direction relay, the problem that the relay protection system misjudges the fault current of multiple power supply ends is effectively solved, and wrong circuit breaker tripping events are reduced. The optimized fault processing logic can isolate a fault area more quickly, power supply to a non-fault area is recovered, and the reliability and stability of a power grid are improved.

The invention relates to the technical field of sealing monitoring, and particularly discloses a tuyere small sleeve sealing monitoring method which specifically comprises the following steps: step 1, in a monitoring period, acquiring pressure data of a tuyere small sleeve at each monitoring time point, and analyzing to acquire an abnormal monitoring time point; 2, acquiring abnormal data of the abnormal monitoring time point based on the acquired abnormal monitoring time point, analyzing the abnormal data, and calculating a pressure abnormal evaluation value; wherein the abnormal data comprises an abnormal monitoring time point number proportion and an abnormal pressure deviation value degree ratio total value. Therefore, abnormity can be detected before the sealing performance is obviously reduced, measures can be taken as early as possible, problem deterioration is reduced, a pressure abnormity evaluation value is calculated based on abnormal data of an abnormal monitoring point, the sealing performance of the tuyere small sleeve can be quantitatively evaluated, maintenance personnel can know the sealing condition of the tuyere small sleeve more visually, and the maintenance efficiency is improved. And the production efficiency and the product quality are improved.

The invention discloses a high-stability ultra-long main shaft of a numerical control machine tool. The high-stability ultra-long main shaft comprises a shell I, a shell II and a rotating shaft, the first shell and the second shell are combined to form a main shaft shell, the rotating shaft penetrates into the main shaft shell, and a plurality of bearings are installed at the two ends of the rotating shaft. A first mounting part, a hollow part and a second mounting part are arranged in the main shaft shell, the hollow part at least accounts for 1 / 2 of the length of the main shaft shell, the first mounting part and the second mounting part are each provided with two clamping positions used for fixing bearings at intervals, and a sound sensor or / and a vibration sensor is / are arranged between the two clamping positions and used for detecting the states of the two bearings; the two opposite bearings located on the first installation part and the second installation part are located on the two sides of the hollowed-out part respectively, the hollowed-out part is defined to form a closed space, the first shell and the second shell are each provided with an air channel, one end of each air channel is connected with the hollowed-out part, and the other end of each air channel extends to the surface of the first shell and the surface of the second shell to be connected with an air nozzle. Gas is pumped out to form negative pressure in the hollow-out part, gas disturbance generated during rotation of the rotating shaft is reduced, gas in the hollow-out part is pumped out from the gas channel through an external device, negative pressure is formed in the hollow-out part, gas disturbance generated during rotation of the rotating shaft can be reduced, and the conditions of pressure rise caused by gas thermal expansion and the like can be reduced; and the stability of the spindle is further improved.

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 provides a new energy equipment fault diagnosis system based on deep learning. According to the system, waveform data of an electronic power device in the new energy equipment is collected and analyzed, so that real-time detection and classification of equipment faults are realized. Specifically, the system firstly collects voltage and current waveforms of a power device through a sensor, and transmits the voltage and current waveforms as input signals to a convolutional neural network. And the convolutional neural network automatically extracts features in the signals through multi-layer convolution and pooling operation, outputs a classification result through a full connection layer, and identifies possible fault types of the equipment. The system can judge the credibility of the classification result through a preset threshold value, if the result is credible, a fault alarm or suggestion is output, and otherwise, manual inspection is further carried out. The method has the characteristics of high precision, high real-time performance and high automation degree, and can be widely applied to fault diagnosis and predictive maintenance of new energy equipment such as wind power, photovoltaic power generation, electric automobiles and the like.

The invention provides a water conservancy safety detection method based on AI edge calculation, and the method comprises the steps: deploying a sensor in a water conservancy system according to the installation and coding of the sensor, coding the sensor, and recording the geographic position information and measurement physical quantity of the sensor; setting measurement parameters, and setting a measurement interval value and a threshold value of each sensor; data linkage setting: defining data linkage selection data, and triggering pump IO output when the data reaches a specific interval threshold value; the method comprises the following steps: monitoring sensor data in real time, if the data is abnormal, starting a video AI to check, comparing whether the result is consistent, if the video AI confirms that the sensor data is abnormal, intercepting a real-time video as sample data, sending the sample data to a cloud server, triggering a corresponding alarm according to the abnormal condition, and notifying related personnel to process. The accuracy and timeliness of abnormal condition detection are improved, and a powerful guarantee is provided for safe operation of a water conservancy system.

The invention discloses a rural photovoltaic grid-connected management method and system, relates to the technical field of energy management, and solves the problem that a harmonic change rate pollution value, an energy loss rate and an environmental influence coefficient are difficult to analyze according to operation data and environmental data. Secondly, potential faults of the photovoltaic power station are difficult to identify and analyze according to video images; thirdly, fault coefficients are analyzed according to the recognition result, and the inspection frequency is planned; and finally, comprehensive analysis is difficult to carry out according to various data, and the inspection frequency is difficult to dynamically adjust after threshold evaluation operation states are divided according to analysis results. According to the invention, real-time fault identification is realized by analyzing operation data and environment data and by means of a deep learning model, and the inspection frequency is planned according to a fault coefficient; the operation condition of the photovoltaic power station is analyzed in multiple dimensions, the operation level is dynamically divided, the inspection frequency is dynamically adjusted accordingly, and the inspection efficiency is improved.

The invention provides a multi-strategy fusion water-turbine generator set fault diagnosis method. The method comprises the following steps: S1, constructing a time sequence model based on an industrial internet platform; s2, constructing a decision model based on industrial internet platform data; s3, using the trained GRU model and the generated optimal random forest to construct a comprehensive model; and S4, performing fault diagnosis on the working condition of the current unit by using the real-time data of the industrial internet platform. Historical data and real-time data in multi-source data of an industrial internet platform are adopted, and a GRU recurrent neural network and a random forest are adopted. A multi-strategy fusion mode is adopted, a GRU-based neural network time sequence model and a random forest decision model are combined to construct a comprehensive model, the advantages of different algorithms are fully utilized, the limitation of fault judgment by a single algorithm is avoided, and the accuracy of fault diagnosis is improved.