1359results about How to "Guaranteed safe operation" patented technology

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

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention relates to the technical field of data processing, in particular to a data processing method and system for a flexible regulation and control terminal, and the method comprises the steps: determining a fuzzy entropy value of each voltage data point in voltage data according to a fuzzy entropy calculation method, and carrying out the abnormality judgment according to the fuzzy entropy value, determining a voltage data point window where the voltage data is located at the sampling moment; calculating a first stability degree according to the quartile distance of the voltage data in the voltage data point window and the number of extreme points; calculating a second stability degree according to the similarity of the voltage data segment and the current data segment in the voltage data point window; and according to the second stability degree, calculating to obtain the sub-window length in the voltage data point window. According to the scheme, the accuracy of fuzzy entropy calculation and the reliability of anomaly detection are remarkably improved.

The invention discloses a leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter, and belongs to the technical field of leakage current monitoring. The invention discloses a leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter, and the system comprises a sensing module, a data collection and preprocessing module, a central processing module, a multi-stage dynamic threshold module, a complex mode recognition module, and a multi-stage early warning module. And real-time early warning cannot be realized, and a complex leakage current mode cannot be identified. According to the environment parameters monitored in real time and the working state of the inverter, the threshold value of the leakage current is dynamically adjusted, the complex leakage current mode is recognized by adopting a machine learning and mode recognition algorithm in combination with the comprehensive analysis result of the zero-sequence current, the phase and the frequency parameters, and the reliability of the inverter is improved. The multi-level early warning module can give alarms of different levels according to the degree of the leakage current.

The invention discloses an unmanned aerial vehicle full-coverage path planning method, system and device and a storage medium, and relates to the field of unmanned aerial vehicles. The method comprises the following steps: acquiring preset map information of a first target area, and determining a second target area for delivering materials according to the preset map information; determining a relay unmanned aerial vehicle set according to the data information, a preset starting point, a second target area, preset map information, a first communication distance of the first unmanned aerial vehicle and a second communication distance of the relay unmanned aerial vehicle, and determining a second initial path of the second unmanned aerial vehicle according to the first initial path; controlling the first unmanned aerial vehicle and the second unmanned aerial vehicle to fly according to a preset initial path, and adjusting the preset initial path according to the environmental data collected by the first unmanned aerial vehicle; and when the first unmanned aerial vehicle arrives at a preset terminal point, determining flight paths of other unmanned aerial vehicles according to the actual load weights and types of the other unmanned aerial vehicles in the transportation unmanned aerial vehicle group and the environment data. By implementing the technical scheme provided by the invention, the task execution efficiency of the unmanned aerial vehicle group is improved.

The invention relates to an intelligent integrated modular cabinet control method, system and device and a medium, and the method comprises the steps: monitoring the external environment parameters of a cabinet in real time through a sensor group in the cabinet, and carrying out the preprocessing of the external environment parameters through an edge computing device, and obtaining multi-dimensional environment parameters; integrating and analyzing the multi-dimensional environmental parameters and the internal operation data of the cabinet by using a multi-modal data fusion algorithm, and outputting a fusion analysis result; according to a fusion analysis result, generating a digital twinborn model of the current environment and the equipment state of the cabinet in the virtual environment, and performing rapid fusion simulation analysis on different adjustment schemes through simulation drift and a genetic algorithm to obtain an optimized adjustment scheme; and adjusting the resource configuration of each module in the cabinet and the operation state of the heat dissipation equipment based on the optimization adjustment scheme. The equipment cabinet has the effect of improving the operation efficiency of the equipment cabinet.

The invention discloses a high-voltage cable distributed continuous monitoring big data analysis processing method, and relates to the technical field of high-voltage cable operation monitoring and fault early warning. Calculating a partial discharge pulse average value, a current harmonic component and a voltage fluctuation amplitude in sequence by combining a temperature change rate and the like; calculating a predicted temperature influence index according to the temperature change rate, the temperature data and the operation state data, sequentially calculating a predicted partial discharge influence index, a predicted current influence index and a predicted voltage influence index in combination with the predicted temperature influence index, and calculating an operation basic index according to the basic parameter data; and finally, calculating a cable operation risk index according to the operation basic index and the predicted voltage influence index, setting a risk index threshold set, judging whether to trigger early warning or not according to a comparison result, and generating an adjustment instruction and a risk assessment report so as to guarantee safe operation of the high-voltage cable.

The invention discloses a reservoir dam safety monitoring abnormity early warning method and system, and belongs to the technical field of safety monitoring, and the method specifically comprises the steps: installing a sensor array at a spillway, a dam body, a dam foundation and a dam shoulder part of a reservoir dam, collecting dam physical parameter data in real time, and transmitting the data to a monitoring center in a wireless manner; after receiving the data, the monitoring center preprocesses and analyzes the data, calculates parameters such as deformation, settlement and displacement of the dam, and compares the parameters with preset safety thresholds; if the dam is in an unsafe state, the system automatically sends out an early warning signal, and a worker acts according to an emergency plan; the method further comprises the steps of field inspection, treatment measure implementation and result recording and feedback, safe operation of the dam is ensured, and the early warning and emergency response efficiency is improved.

The invention provides an inertia optimization method for electric heat gas energy transport channel coordination, and relates to the technical field of energy transport channels, and the method comprises the steps: building a fluid flow state and vortex intensity mapping relation model, which is used for predicting the possible vortex conditions under different working conditions; and based on a prediction result, a genetic algorithm is adopted to optimize a sensor arrangement scheme, and the most comprehensive flow state information is obtained by using the least sensors. According to the method, pipeline vibration safety is considered, a vibration monitoring and valve adjustment limiting mechanism is set, in addition, a tradeoff model of energy loss and transportation efficiency is further constructed, an optimal adjustment strategy is solved by applying a multi-objective optimization algorithm, finally, an eddy current suppression effect evaluation model is established, quantitative evaluation and strategy optimization of a control effect are achieved, and the control effect is optimized. Through the comprehensive application of a series of innovative methods, the precise inhibition of the vortex in the fluid transportation process and the remarkable improvement of the transportation efficiency are realized, and meanwhile, the safe operation of a pipeline system is ensured.

The invention relates to the technical field of power distribution cabinets, and discloses a power distribution cabinet with a self-adaptive environment adjusting mechanism, and the power distribution cabinet comprises a cabinet body which is used for fixing and supporting internal electrical elements; the automatic dehumidification assembly is mounted on the outer side of the cabinet body and is used for dehumidifying the cabinet body when detecting that the moisture content in the cabinet body is too high; the cleaning assembly is installed on one side of the cabinet body and used for conducting anti-blocking treatment on a filter screen arranged outside the cabinet body, and the driving assembly is installed outside the cleaning assembly and used for driving the cleaning assembly. By adding the automatic dehumidification assembly, the humidity in the power distribution cabinet can be effectively reduced, and the risk of electrical short circuit caused by a humid environment is reduced. By discharging moisture, safe operation of electrical equipment is guaranteed, and fire and electric shock accidents caused by moisture are avoided. And meanwhile, corrosion and aging processes can be slowed down, the service lives of the power distribution cabinet and internal components are remarkably prolonged, and the maintenance and replacement cost is reduced.

The invention relates to the technical field of fuel gas safety, and discloses an intelligent fuel gas safety valve and a method for intelligently opening and closing the safety valve, and the intelligent fuel gas safety valve comprises a sensor unit which is configured to be installed near a fuel gas pipeline or fuel gas equipment; the control unit is in communication connection with the sensor unit; the execution unit is in communication connection with the control unit; the communication unit is in communication connection with the control unit; and the standby power supply is used for providing emergency power for the intelligent gas safety valve when the main power supply fails or is powered off. The concentration, the pressure, the temperature and the flow of the fuel gas are monitored in real time through the sensor unit, the system can quickly send out an instruction for closing the valve when detecting abnormal conditions (such as gas leakage and too high or too low pressure), and accidents such as fire disasters or explosions caused by fuel gas leakage are effectively prevented. The system monitors a plurality of key parameters of the gas system through a plurality of sensors in a combined mode, and a multi-level safety guarantee mechanism is formed.

The invention relates to a low-altitude economic development form analysis method and system based on a high-density city, and the method comprises the steps: obtaining task address information and task type information of each aircraft, and carrying out the path planning in a preset airspace range according to the task address information and the task type information, obtaining flight path information of each aircraft; acquiring real-time traffic flow and airspace condition information in a preset airspace range, and performing real-time optimization adjustment on the flight path information according to the real-time traffic flow and the airspace condition information; monitoring the activities of the aircrafts in the airspace in real time, obtaining the flight information of each aircraft, and judging whether the corresponding aircraft has a flight violation condition or not according to the flight information; the flight task state is tracked and fed back in real time, the aircraft state and the equipment operation condition in the airspace are monitored, equipment alarm is triggered under the abnormal condition, and preset emergency treatment measures are taken. The method has the effect of improving the low-altitude aircraft management and control efficiency.

The invention discloses a temperature control method for an intelligent power module based on a gallium nitride power chip, and relates to the technical field of module control, and the method comprises the steps: collecting the temperature data of the intelligent power module through a distributed temperature sensor, carrying out the preprocessing, and carrying out the dynamic power path adjustment of the intelligent power module according to the temperature data; predicting a future temperature based on the power path adjusted by the intelligent power module by using an intelligent algorithm, and performing adjustment according to a prediction result; the intelligent power module cooling system is optimized in combination with the temperature data and the prediction result; and displaying the temperature data and the power path in real time, and storing the temperature data and the power path in a database. According to the method, safe operation of the intelligent power module is guaranteed, accurate adjustment of the power is achieved, meanwhile, the power adjustment effect and cooling system optimization are verified by predicting the future temperature, and the accuracy and flexibility of power adjustment are further improved.

The invention relates to the technical field of metal bearings, in particular to a reinforced liquid metal sliding bearing. The enhanced liquid metal sliding bearing comprises a rotor, a stator and liquid metal filled in an assembling gap between the rotor and the stator. The fixed positions of the rotor and the stator are provided with a spiral groove structure, a textured microstructure, an infiltration coating and a sealing coating. By adopting the enhanced liquid metal sliding bearing provided by the invention, the wettability and the wear resistance of a bearing rotating pair can be improved, and the problems of coating falling, wear, poor lubrication and the like of the enhanced liquid metal sliding bearing in a high-vacuum, high-temperature, high-pressure, high-speed and high-load use environment are effectively solved. High-pressure sealing is formed on the gaps between the stator and the rotor and between the limiting rings of the stator and the rotor, the liquid metal is kept in the bearing, the assembled bearing does not need a vacuum environment in the transfer process, meanwhile, external gas is prevented from entering the bearing structure, and the possibility that the liquid metal leaks or gets stuck is reduced.

The invention discloses an intelligent hydrogen production system and method.The intelligent hydrogen production system comprises a water supply device, a hydrogen production device, a purification device, a hydrogen conveying device, a pressure relief device, a system monitoring unit and a system control unit.The liquid level of a water tank is detected through a first liquid level meter, and the temperature of pure water of the water tank is detected through a first temperature sensor; the conductivity of water treated by the water machine is detected through the first conductivity meter so as to judge the purity degree of the water, the conductivity of electrolyzed water entering the electrolytic bath is detected through the second conductivity meter, the temperature of the electrolyzed water in the electrolytic bath is detected through the second temperature sensor, and it is guaranteed that the water quality and the water temperature in the electrolytic bath meet the electrolysis requirement. The liquid level of separated water in the water-vapor separator is detected through the second liquid level meter, the dew-point temperature of hydrogen is detected through the dew-point meter, and the actual output flow of hydrogen flowing through the hydrogen outlet is detected through the flow meter. The water temperature, the water level, the hydrogen flow, the conductivity and the like can be monitored, safe and efficient operation of equipment is ensured, and potential safety hazards are prevented.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator set with a vibration reduction function and a construction method thereof.According to the wind turbine generator set with the vibration reduction function, a vibration reduction structure is additionally arranged, and the two elastic ends, arranged in the axial direction, of an elastic buffering piece are connected with the outer wall of a tower drum and the inner wall of a sleeve correspondingly; the elastic buffering piece can effectively absorb and relieve vibration of the sleeve, vibration impact borne by the tower drum is reduced, the overall stability of the wind turbine generator is improved, and safe and stable movement of the wind turbine generator is guaranteed. Compared with a conventional scheme that the diameter or the wall thickness of the tower drum is increased, the damping structure is simple in structure, convenient to install and maintain and low in cost.

The invention provides a power transmission line windage yaw galloping monitoring and three-dimensional imaging system based on a laser triangulation method, and relates to the technical field of laser ranging, and the system comprises a windage yaw galloping excavation assembly which is used for carrying out windage yaw galloping partition division and windage yaw galloping relation excavation; the windage yaw galloping monitoring module configuration assembly is used for configuring a windage yaw galloping monitoring module; the laser detection data determination assembly is used for determining laser detection data; the three-dimensional imaging module construction component is used for constructing a three-dimensional imaging module; the three-dimensional windage yaw galloping model determining assembly is used for determining a three-dimensional windage yaw galloping model; and the monitoring and early warning assembly is used for carrying out power transmission line monitoring feedback and windage yaw galloping early warning. The technical problems that the power transmission line windage yaw galloping monitoring precision is low and the three-dimensional motion information of the power transmission line cannot be accurately obtained can be solved, and the technical effects of improving the power transmission line windage yaw galloping monitoring precision and ensuring the accuracy of windage yaw galloping early warning by obtaining the dynamic three-dimensional image of the power transmission line are achieved.

The invention discloses a CFD-based liquefied natural gas after-condenser structure optimization method and device. The method comprises the steps that an initial model is established based on an assembly drawing, and geometric pretreatment is completed; extracting a fluid channel, segmenting and selecting a computational domain, and carrying out local encryption on a key region; generating a polyhedral grid by using a grid division tool, and completing grid independence verification; importing the polyhedral mesh into a solver, and carrying out model setting; boundary and wall surface conditions are set, and solver parameters are configured; setting an initial flow field through a hybrid initialization method, and establishing a flow monitoring window and a temperature monitoring window to adjust simulation until convergence; analyzing the temperature cloud picture and the flow vector diagram, and adjusting the structure of the baffle plate; the tower diameter and the height of the filler layer are adjusted according to a physical quantity field change curve of the filler layer, and the performance of the recondenser is continuously improved. The problem of how to deeply study the flow heat transfer rule in the after-condenser and obtain the flow field distribution characteristics is solved, so that the working performance of the after-condenser is improved.

The present invention relates to the technical field of wellhead sealing, and is a packing automatic sealing self-tightening system, comprising a left valve core, a right valve core, a first drive assembly, a sealing packing, a second drive assembly and a cross-shaped sealing housing. The present invention has a reasonable and compact structure. When the light rod in the penetration hole is not in the center, the right valve core moves to the left, so that the eccentric light rod can move along the inner wall of the guide groove to the right sealing groove, which has a straightening effect on the eccentric light rod, and then the guide part moves to the right again to seal the guide groove, and finally the outer side of the light rod is in sealing contact with the inner wall of the sealing hole, thereby playing the effect of closing the penetration hole. When the sealing packing on site is punctured, the second drive assembly works and compresses the sealing packing through the compression sleeve, thereby preventing and stopping the sealing packing from continuing to puncture. When the light rod leaks or steam blowout occurs, the penetration hole can be quickly and timely closed by the first drive assembly and the second drive assembly, thereby ensuring the safe operation of the wellhead blowout preventer of the pumping unit and reducing the occurrence of wellhead pollution events.

The invention relates to the technical field of tunneling parameter prediction, in particular to a complex stratum TBM tunneling parameter real-time prediction method. According to the technical scheme, the method comprises the steps of data collection and preprocessing, model selection and training, model prediction and evaluation, and result application and feedback. According to the data collection and preprocessing, operation parameters are collected in real time through various sensors on TBM equipment, wherein the operation parameters comprise the cutterhead rotating speed, the penetration degree, the thrust, the torque, the total thrust, the gripper shoe pressure, the propelling speed, the current and the power. Real-time prediction of the TBM tunneling parameters of the complex stratum is achieved by fusing the machine learning algorithm, the tunneling parameters can be optimized in real time, and the construction efficiency is improved; the tunneling process is stabilized to guarantee the engineering quality; tool abrasion is reduced, energy consumption is reduced, and the construction cost is reduced; equipment faults and accidents are prevented, and construction safety is guaranteed; and construction technology innovation can be promoted, intelligent construction management is achieved, the comprehensive benefits of TBM construction are comprehensively improved, and efficient, safe and intelligent development of tunnel (roadway) engineering is promoted.

The invention relates to the technical field of robot teaching-free welding, and particularly discloses a robot teaching-free welding system based on visual localization and a welding method.The robot teaching-free welding system comprises a PLC, a power module, a wireless communication module and a visual localization coordinate conversion module. The path planning optimization module directly generates a welding path according to a visual positioning result, tedious operation is reduced, the problem of poor dependence and adaptability on pre-stored data is solved, the visual acquisition module and the positioning module work cooperatively, workpiece position and posture information is updated in real time, and the positioning precision is improved. The teaching-free intelligent decision-making and optimization module is combined with real-time data to adjust a path and parameters, the image processing and path planning optimization module generates a proper path according to image information for a complex workpiece, the sensor fusion module fuses vision and other sensor data, the adaptability of the system to the complex workpiece is enhanced, and the welding quality is guaranteed.

The invention discloses an intelligent emergency control system of an arrangement ship steering oar, and belongs to the technical field of arrangement ship steering oar control. An intelligent emergency control system of an arrangement ship steering oar comprises a main control module, a steering oar driving module and a steering oar control module. The problem that in the prior art, when a main steering system breaks down, a ship is prone to being out of control, colliding or other safety accidents are caused is solved, the steering oar driving module is used for conducting driving operation on the arrangement ship steering oar in an emergency mode, the state of the arrangement ship steering oar is monitored in real time, and the safety of the arrangement ship steering oar is improved. When potential problems and fault trends exist in the arrangement ship steering oar, a control instruction of the arrangement ship steering oar is adjusted so as to ensure safe operation of the arrangement ship steering oar, quick response can be made and an emergency mode can be started in emergency, dangerous situations such as out-of-control of the ship can be effectively avoided, remote monitoring and management can be achieved, and the safety of the arrangement ship steering oar is improved. Safety and management efficiency of the ship are improved, and safe driving of the ship is effectively guaranteed.

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 technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention provides a motor stator vibration suppression method and system based on an electromagnetic pad and deep learning, and relates to the field of electrical technology, and the method comprises the steps: collecting a vibration signal of a motor stator, and carrying out the preprocessing of the vibration signal; inputting the preprocessed signal into a deep learning model, and training and optimizing the deep learning model; fusing the features through a full connection layer to obtain a comprehensive feature vector; predicting the future vibration state of the motor stator by using the comprehensive feature vector; establishing an electromagnetic force model, and designing an adaptive control law based on the predicted future vibration state and the electromagnetic force model; electromagnetic force required by vibration suppression is calculated by using a self-adaptive control law and is converted into a control signal to adjust the electromagnetic pad; a multi-objective optimization algorithm is used to consider the vibration suppression effect and energy consumption at the same time, an adaptive control law is adjusted, and closed-loop feedback control is formed to continuously optimize the vibration suppression effect.

The invention belongs to the technical field of aging state evaluation, and discloses a high-voltage dry-type bushing insulation aging state evaluation method based on frequency domain characteristics. According to the method, the FDS is adopted, and the aging state of the insulating material is evaluated according to the change of the complex capacitance, the complex dielectric constant, the dielectric loss factor and the like of a medium along with the frequency under a low-voltage sine alternating electric field. The FDS shows remarkable superiority in a plurality of aspects. The method comprises the following steps: firstly, measuring a dielectric constant and dielectric loss of an insulating medium in a wide frequency range by an FDS method so as to obtain response characteristics of the insulating material under different frequencies; according to the method, the frequency domain dielectric spectroscopy FDS is combined with the advantages of wide frequency range measurement, frequency domain feature recognition, comprehensive reflection of electrical characteristics and the like, so that accurate evaluation of the insulation aging state of the high-voltage dry-type bushing is realized. The safety and the stability of a power system can be improved, and faults and accidents caused by insulation aging can be reduced.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention provides a multi-model fusion asynchronous motor fault diagnosis method. The method comprises the following steps: converting a collected asynchronous motor time domain signal into a frequency domain signal by adopting fast Fourier transform; extracting a plurality of characteristic values related to the motor fault from the frequency domain signal; the current working mode of the motor is determined by calculating the power frequency and the load rate of motor operation; using all normal samples to train a main model as a reference model; and for each working mode, training the sub-model by using the corresponding sample data. According to the working mode of the to-be-predicted sample, the corresponding sub-model is preferentially selected for calculation, and if the matched sub-model cannot be found, the main model is used for calculation. And calculating the fault probability of each component of the motor according to the abnormal probability of the characteristic value predicted and calculated by the model. The fault diagnosis method can effectively meet the fault diagnosis requirements in different working modes, is particularly suitable for fault diagnosis of the variable frequency motor under complex working conditions, and improves the accuracy and adaptability of diagnosis.

The invention belongs to the field of electric power automation, and discloses an electric energy and climbing demand capacity combined clearing method and a related device, and the method comprises the steps: obtaining the climbing demand capacity of a power system based on the distribution of historical net load prediction errors of the power system; obtaining the climbing demand vacancy cost of the power system according to the climbing demand capacity of the power system; performing day-ahead clearing according to the day-ahead load prediction value and the day-ahead new energy output prediction value of the power system to obtain a day-ahead clearing result; and performing intra-day rolling clearing according to the day-ahead clearing result in combination with the intra-day load prediction value and the intra-day new energy output prediction value of the power system to obtain an intra-day rolling clearing result. According to the method, the possible climbing demand of the power system in the future can be reflected more accurately, the energy storage station and the flexible load are fully utilized to reduce the net load fluctuation in the day-ahead market, the flexible climbing ability with the finer time scale is provided in the day-ahead market wind and light uncertain environment, and the uncertainty fluctuation risk resistance ability is enhanced.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project digital management method based on a BIM model. The method comprises the following steps: acquiring real-time monitoring data; carrying out high-precision topographic data acquisition on a water conservancy project area, and carrying out data preprocessing to obtain preprocessed data; according to the preprocessed data, hydrological information integration modeling is carried out by utilizing a BIM platform, and a hydrological information integration model is obtained; performing multi-dimensional terrain dynamic updating on the hydrological information integration model by using the real-time monitoring data to obtain digital terrain data; performing flood simulation calculation based on the digital topographic data to obtain a flood simulation calculation result; and performing flood scene simulation according to the flood simulation calculation result to obtain flood simulation data. Through real-time monitoring, accurate prediction and timely early warning of the risk of the water conservancy project, a more effective technical means is provided for flood control and disaster reduction and safety management of the water conservancy project.