647results about How to "Avoid downtime" patented technology

The invention, which relates to the technical field of the industrial internet, discloses an intelligent equipment data interaction system based on the industrial internet, comprising an industrial internet platform. The industrial internet platform is in communication connection with a data acquisition and preprocessing module, an edge calculation module, a data transmission module, an anomaly analysis module, an intelligent decision module, a feedback control module and a man-machine interaction module, and all the modules are in electric signal connection. According to the intelligent equipment data interaction system based on the industrial internet, the production efficiency is remarkably improved by monitoring and optimizing the production process in real time, data are collected through a sensor and an equipment state monitor, and abnormal states in the production process are quickly recognized and responded through preprocessing and analysis of an edge calculation module; and the intelligent decision-making module generates an optimization control scheme according to an analysis result and feeds back the implementation scheme of the control module, so that the risks of equipment failure and production interruption are reduced.

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

The invention discloses a 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 relates to the technical field of automobile data updating, in particular to a data updating method of automobile diagnosis equipment and the automobile diagnosis equipment. The method comprises the following steps: acquiring a historical running log of a vehicle; vehicle owner driving behavior multi-point sampling is carried out on the vehicle historical operation log, and different scene behavior characteristic evolution is carried out, so that different scene driving behavior models of a vehicle owner are obtained; extracting vehicle time sequence state parameters based on the vehicle historical operation log; performing multi-scene operation simulation on the vehicle time sequence state parameters according to different scene driving behavior models of the vehicle owner, and generating vehicle state operation simulation data of each scene; and carrying out future time period vehicle state rolling prediction and fault time window probability calculation on the vehicle state operation simulation data of each scene to obtain a vehicle abnormal fault part positioning point and a fault time window probability curve. According to the invention, the data updating efficiency and group accuracy of the automobile diagnosis equipment are realized.

The invention relates to the technical field of lithium batteries, and discloses a lithium battery life decline quantitative prediction and health state intelligent evaluation method, which comprises the following steps: S1, multi-source data acquisition: arranging strain gauge sensors at battery electrode plates and a shell to acquire stress-strain data, integrating a gas sensor in a battery module to acquire gas data, and calculating the data of the stress-strain data; battery data and environment data are acquired by a high-precision synchronous acquisition system through nanosecond clock signals and are processed and stored, and the data quality is monitored at the same time; s2, data preprocessing: generating an extended data set through data transformation and combination and GAN-based data, and processing abnormal data by using a method based on statistics and machine learning; according to the invention, through multi-source data fusion acquisition, a stress-strain sensor and a plurality of gas sensors are added to monitor the internal mechanical change and gas component concentration of the battery, so that the problem that the accuracy and practicability of the model are greatly reduced due to a traditional method based on an electrochemical model is improved.

The invention relates to the technical field of air conditioner energy-saving systems, and discloses a central air conditioner energy-saving automatic control system and method.The central air conditioner energy-saving automatic control system comprises a sensor module, a control module, a communication module, an intelligent control algorithm module, a dynamic adjusting module and an energy-saving optimization module; the sensor module comprises a temperature sensor, a humidity sensor, a CO2 sensor, an illumination sensor and an occupation sensor, the temperature sensor is used for monitoring indoor and outdoor temperatures and providing real-time data for decision making of a control system, and an NTC (negative temperature coefficient thermistor), a PTC (positive temperature coefficient thermistor) or a DS18B20 digital temperature sensor is adopted. The environment parameters are monitored in real time, the operation state of the air conditioning equipment is dynamically adjusted, the high-energy-consumption operation mode of a traditional air conditioning system is avoided, cooling / heating according to needs is achieved, energy consumption is effectively reduced, the mode is automatically switched to the energy-saving mode in the state that the number of personnel activities is small or no one exists, and therefore the effect of remarkably reducing the operation load and energy consumption can be achieved.

The invention discloses a life prediction method, device and system for a chain transmission system oriented to multi-component coupling, and the method comprises the steps: carrying out the topological structure decomposition of multiple components of the chain transmission system, and extracting the fault relation between the components of the system through combining historical fault data and expert knowledge; preprocessing the fault data to obtain a structured fault feature matrix; establishing a fault propagation network of the chain transmission system by using the cloud model symbol directed graph, and constructing a modularized fault propagation path reasoning model based on the functional relationship and working condition change of system components; dynamic simulation and real-time analysis are carried out on fault propagation of the system through compatible pathway search and qualitative and quantitative conversion of a cloud model; the random uncertainty and the cognitive uncertainty are quantified in a unified mode, and a regression model is adopted to conduct modeling on the degradation trend of the chain transmission system; and performing model parameter optimization, performing residual life prediction of the chain transmission system in combination with a first arrival time method, and giving out the confidence coefficient of life prediction through confidence interval analysis.

The invention discloses a public vehicle operation management method based on big data analysis and related equipment, and the method comprises the steps: receiving a battery alarm signal of an electric public vehicle, and obtaining a battery parameter and vehicle operation data; inputting the current environment data and the real-time road condition data into a big data model to obtain a current operation scene of the vehicle; inquiring to obtain a battery parameter interval corresponding to the current operation scene of the vehicle; under the condition that the battery parameters are located in the battery parameter interval, vehicle energy consumption data in the preset kilometer interval are obtained, and according to the battery parameters, the vehicle energy consumption data and the current operation scene of the vehicle, the energy consumption trend of the electric public vehicle in the future preset time period is predicted; generating a power output mode of the electric public vehicle according to the energy consumption trend, the vehicle operation data and the driving behavior data; generating a brake adjusting instruction according to the power output mode; and the brake adjusting instruction is sent to the vehicle terminal, so that the vehicle terminal executes a brake strategy.

The invention provides a multi-path main road signal cooperative control method considering speed fluctuation, and the method comprises the steps: firstly collecting traffic data, including road network geometric data, traffic flow data and signal control data, and determining a plurality of main paths according to the traffic flow data; then, based on the running speed of each vehicle on each main path, the vehicle running average speed and the speed fluctuation quantity on each main path are calculated; and finally, constructing a signal control optimization model: constructing a target function by taking weighted green wave bandwidth maximization of all paths as a target, adding a loop integer constraint, an interference constraint, a speed fluctuation space-time constraint, a highest speed fluctuation constraint and a lowest speed fluctuation constraint, solving the signal control optimization model, and obtaining an optimized signal period and green wave bandwidth. And outputting the optimized signal timing strategy. The signal timing generated by the method has a good prospect in the aspect of reducing travel delay and parking times, and a better multi-path collaborative effect can be realized.

The invention relates to the technical field of equipment fault diagnosis, and discloses a steam turbine vibration fault risk evaluation method and system.The method comprises the following steps that real-time data such as vibration, temperature, pressure and rotating speed of a steam turbine are collected through multiple sensors; the sensors comprise an accelerometer, a displacement sensor, a temperature sensor, a pressure sensor and a rotating speed sensor; the collected multi-source data is preprocessed, including de-noising, time synchronization and data standardization, to eliminate environmental noise and time difference between the data. The technical scheme based on multi-dimensional data acquisition and fusion is adopted, vibration sensors, temperature sensors, pressure sensors, rotating speed sensors and the like are arranged at key parts of a steam turbine, the running state of equipment is obtained in real time, multi-dimensional fusion and feature extraction are carried out on data, and the effects of comprehensive and real-time monitoring and early prediction of vibration faults of the steam turbine are achieved.

The invention relates to an air compressor fault diagnosis method and system and electronic device.The air compressor fault diagnosis method comprises the steps that current operation parameters of an air compressor are obtained, the current operation parameters are preprocessed, and input data are obtained, the operation parameters comprise a vibration parameter, a temperature parameter, an input pressure, an output pressure and a working current of the motor; according to the historical operation parameters and historical fault type data of the air compressor, a Transform model is trained to obtain a fault diagnosis model, the fault diagnosis model comprises multiple attention modules, and the multiple attention modules are used for calculating attention scores; and the state of the air compressor is diagnosed according to the input data and the fault diagnosis model, a diagnosis result is obtained, target data concerned during diagnosis is obtained according to the attention score, and supplementary interpretation is conducted on the diagnosis result.

The invention provides a micro-nano impact press-in tester operation monitoring system based on digital twinning, and is suitable for the field of precise instrument maintenance. The system realizes multi-physical field coupling modeling in a micro-nano press-in test through a digital twinning technology, and can monitor the state of an instrument in real time with high precision, predict faults and perform dynamic optimization. The system is integrated with a high-precision sensor, data such as micro force, extremely high temperature and nanoscale displacement are collected in real time, a digital twin model is constructed, and the operation state of an instrument is mapped. Historical and real-time data are analyzed by means of a machine learning algorithm, potential faults can be accurately predicted, maintenance suggestions can be provided, and the precision retentivity and the test stability of the instrument under the material service working condition are ensured. The interaction center provides a visual interface, supports real-time monitoring, dynamic adjustment and early warning functions, and remarkably improves the intelligent maintenance level of the instrument. The invention is suitable for the fields of material science, micro-nano mechanical test and the like, and provides an innovative precise instrument intelligent management scheme.

The invention relates to the technical field of new energy station fault diagnosis, in particular to a new energy station fault diagnosis system and method based on an intelligent sensor, and the system comprises an intelligent processing unit which collects operation parameters in a station in real time, carries out the fault diagnosis of the operation parameters, and obtains diagnosis data; when the method is used, potential problems of equipment can be found in time, the data transmission bandwidth is effectively reduced, rapid transmission of real-time data is guaranteed, the equipment can adjust the working state in a self-adaptive mode under different operation conditions, false alarm or missing alarm caused by a fixed threshold value is avoided, and the service life of the equipment is prolonged. The influence of a network bottleneck on the overall efficiency of the system is reduced, the response speed and stability of the system are improved, the bandwidth resource waste is reduced, the overall performance of the network is improved, the timeliness and reliability of fault diagnosis are enhanced, meanwhile, the real-time performance and reliability of a data acquisition and monitoring system in a new energy station are greatly improved, and the application prospect is wide. And the potential cause of the fault can be accurately identified.

The invention relates to the technical field of photo-thermal power generation, in particular to a photo-thermal power station heat collection field power generation efficiency evaluation method and system, which can provide more accurate and real-time power generation efficiency evaluation. The method comprises the steps that design parameters, historical operation data and historical heat collection efficiency of a heat collection field of the photo-thermal power station are collected; according to the design parameters of the heat collection field of the photo-thermal power station, a simulation model of the heat collection field is constructed to simulate the operation condition of the heat collection field of the photo-thermal power station, and the simulation model comprises a minute-level mechanism model and an artificial intelligence correction module; inputting the historical operation data into the simulation model to obtain historical predicted heat collection efficiency; performing deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain a model correction index; the model correction index is substituted into the simulation model, the model is corrected, and a heat collection efficiency evaluation model is obtained; and acquiring real-time operation data, and inputting the real-time operation data into the heat collection efficiency evaluation model to obtain the power generation efficiency of the heat collection field.

The invention relates to the technical field of medical apparatuses and instruments, in particular to a medical apparatus and instrument state management system based on the Internet of Things technology, which can remotely monitor and manage medical equipment and medical consumables in the whole period. Through the combination of the equipment sensor, the Internet of Things platform and the mobile terminal application, medical equipment management personnel can know information such as the operation state, the energy consumption condition, the maintenance period and the operation time of the medical equipment in real time, meanwhile, preventive maintenance can be carried out, related faults can be handled in time, and the quality and efficiency of medical services are improved. By means of intelligent remote monitoring and management, information islands are eliminated, efficiency is improved, data unification and sharing among all systems and departments are achieved, and the purpose of conducting real-time remote monitoring and management on medical instruments in the whole period is achieved.

The invention discloses a traveling wave electric curtain lunar dust conveying device based on three-dimensional electrodes, and relates to the field of lunar dust conveying. In order to solve the problems of low utilization rate of an electric field, easy abrasion of equipment and high energy consumption in the existing lunar dust transportation work in the prior art, the technical scheme provided by the invention is as follows: the traveling wave electric curtain lunar dust transportation device based on the three-dimensional electrode comprises a substrate; the wedge-shaped microstructure is arranged on the upper surface of the substrate, and the wedge-shaped microstructure is in a triangular prism shape; and the electrodes are arranged on the side walls of the wedge-shaped microstructures and used for generating electrodes and transporting lunar dust particles. The invention also provides a traveling wave electric curtain lunar dust transportation method based on the three-dimensional electrode, the method is realized based on the device, and the method comprises the following steps: collecting the motion state of lunar dust particles; a step of adjusting a driving voltage according to the motion state; and driving the electrode according to the adjusted driving voltage. The lunar dust conveying device is suitable for lunar dust conveying work.

The invention relates to the technical field of heavy oil thermal recovery, in particular to a multi-element thermal fluid generator system operation control system which comprises a data acquisition module used for acquiring working parameters of a multi-element thermal fluid generator during working, and the working parameters comprise multi-element thermal fluid parameters, working pressure, working temperature, water flow, gas amount and air flow; the trend identification module is used for identifying change trends of the working parameters at different positions, selecting the change trends of the corresponding working parameters according to different types of abnormal states when the working parameters are in abnormal states, and judging fault positions and processing measures of the working parameters according to the change trends; the work adjusting module is used for acquiring the parameter proportion and the distribution quantity of the working parameters according to the fault positions of the working parameters, and setting adjusting factors of the working parameters according to the parameter proportion and the distribution quantity of the working parameters; the stability, reliability and operation efficiency of the system are improved, and the maintenance cost is reduced.

The invention discloses an equipment health management system and method based on artificial intelligence and computer equipment, and the system comprises an equipment data collection module which is used for obtaining equipment environment data transmitted by a sensor disposed in an influence area corresponding to each piece of electrical equipment, and obtaining equipment state data corresponding to each piece of electrical equipment; the data preprocessing module is used for carrying out standardized preprocessing on the equipment environment data and the equipment state data; the fault diagnosis module is used for performing fault diagnosis on the electrical equipment based on the preprocessed equipment environment data and equipment state data so as to identify faulty electrical equipment and generate fault information of the faulty electrical equipment; the research and judgment processing module is used for constructing a fault analysis prompt word according to the fault information, inputting the fault analysis prompt word into a research and judgment large model, and generating fault processing information of the fault electrical equipment through the research and judgment large model; and the fault handling module is used for carrying out fault handling on the fault electrical equipment based on the fault handling information.

The invention relates to the technical field of filtering devices, and particularly discloses a blockage removal type filtering device and a detection method. The filtering device comprises a filtering pipeline, a blockage removing assembly and a detection assembly; the blockage removing assembly is used for conducting blockage removing operation on the filtering pipeline, and the detection assembly is used for monitoring the pressure, the working duration and the liquid flow state of the filtering pipeline in real time. The filter screen covers the filter screen through the ultrasonic blockage removal unit, so that filter residues on the filter screen are removed through kinetic energy generated by bubble breakage. The detection method comprises the following steps: after the filtering device starts working, the detection assembly detects working information of the filtering pipeline; matching is conducted according to the filtering operation information and a preset blockage removal threshold value, and if matching succeeds, blockage removal operation is started; and if the matching fails, continuing the detection operation. By means of multiple monitoring means, it is ensured that stable operation is still achieved under the condition that part of assemblies are disabled, system shutdown caused by faults of a single detection assembly is avoided, and the efficiency of blockage removal operation is effectively improved.

The invention discloses a self-cleaning assembly and method for a dust removal filter bag of a cement coal mill, and relates to the technical field of dust removal and filtration. The bottom end of a first shaft rod of a first expansion piece is connected with a multi-face mounting block, each mounting face of the multi-face mounting block is provided with a fixing sleeve box with an open mounting cavity and a guide through groove, a tension spring is arranged in each open mounting cavity, a movable inserting piece is arranged at the open mounting cavity and the guide through groove and protrudes outwards, and a C-shaped magnetic attraction piece is fixed to the side end of each movable inserting piece. The bottom end of a second shaft rod of the second expansion piece is connected with an annular air injection piece, electromagnetic rings matched with the magnetic attraction parts are further fixed to the upper end and the lower end of the annular air injection piece, a plurality of horizontal air outlet pipes surrounding the dust removal filter bag are arranged in the annular air injection piece, air outlets directly face the filter bag and are connected with an air supply pipeline, and the air supply pipeline is connected with an air supply mechanism. Dynamic cleaning of the dust removal filter bag is achieved, and the problem that a production system stops working due to the cleaning requirement of the dust removal filter bag is solved.

The invention discloses an obstacle avoidance method and system for a medical displacement machine, and the method comprises the steps: collecting and processing static and dynamic obstacle information in an environment, and obtaining first precise obstacle information; classifying the first accurate obstacle information through a convolutional neural network, and recording a first dynamic motion track; learning a dynamic obstacle motion mode by using a recurrent neural network, and outputting a predicted dynamic obstacle trajectory; a static path planning algorithm is adopted on a known map, and a preliminary path from a starting point to a target point is found in combination with the predicted dynamic obstacle trajectory; when it is detected that the moving direction or speed change of the dynamic obstacle is second accurate obstacle information, the second accurate obstacle information is used for outputting and updating the dynamic obstacle track; and outputting an obstacle avoidance path according to the updated dynamic obstacle trajectory. When the moving direction or speed change of the dynamic obstacle is detected, new obstacle information is immediately used to update the trajectory of the dynamic obstacle, and the path is re-planned, so that the real-time performance and effectiveness of obstacle avoidance are ensured.

The invention provides a power chip operation parameter anomaly detection method and system, and relates to the technical field of data processing, and the method comprises the steps: taking an electronic device configured with a power chip as an edge node, taking a cloud server as a cloud platform, building a communication relation between the edge node and the cloud platform, and constructing a cloud edge collaborative anomaly detection architecture; acquiring operation parameters of the power chip at the edge node; at the edge node, whether the power chip in the edge node has a fault risk or not is judged through a rapid fault detection model according to the operation parameters of the power chip; if yes, the power chip is closed immediately, and an early warning prompt is given out; otherwise, uploading the output signal of the power chip to the cloud platform; extracting signal features of the output signal of the power chip at the cloud platform; on the cloud platform, according to the signal characteristics of the output signal of the power chip, whether the power chip has a chronic degradation risk is judged through a chronic degradation detection model; and if yes, sending out an early warning prompt.

The invention relates to the technical field of putty powder production and crushing, and discloses impact crushing equipment based on putty powder production, the impact crushing equipment comprises a crusher, the crusher comprises a main frame, a fixed jaw plate, an outer frame and a movable jaw plate, a crushing cavity is formed between the fixed jaw plate and the movable jaw plate, the main frame is also provided with a driving assembly and a controller, the driving assembly acts on the movable jaw plate, and the controller is connected with the fixed jaw plate. The movable jaw plate is driven to move away from or close to the fixed jaw plate in a reciprocating mode, and hammering mechanisms are symmetrically arranged on the front position and the rear position of the main machine frame. According to the impact crushing equipment based on putty powder production, the hammering mechanism and the control mechanism are arranged, impact force applied by a hammer head is utilized, marble blocks can be further crushed, the clamped marble blocks are loosened, and therefore the clamping and blocking phenomenon caused by the fact that the marble blocks are large in size or irregular in shape can be eliminated; and moreover, through vertical reciprocating hammering of the hammer head, the blocking condition can be quickly handled, so that the manual handling time is further shortened.

The invention discloses a photovoltaic power station monitoring system. Environmental data and equipment state data are collected in real time through a sensor; cleaning, de-noising and standardizing the collected original data; decomposing the generating capacity and the equipment state data through Fourier transform; performing convolution processing on the data through a multi-scale convolutional neural network, and extracting features of different time scales; through an LSTM model, a BP neural network and a GRU model, generating capacity prediction, equipment fault diagnosis and quick response of monitoring equipment are realized; integrating output results of the LSTM model, the BP neural network and the GRU model through weighted fusion to generate a unified decision support result; and through data visualization, generating capacity prediction, an equipment state and a historical trend are displayed, and a customized operation report is generated. According to the system, the intelligent level of a photovoltaic power generation system is effectively improved, and the fault prediction and operation monitoring capabilities of equipment are enhanced.

The utility model provides a skid-mounted mixing system and belongs to the technical field of skid-mounted mixing. Comprising a box body; the stock bin is arranged in the box body; the grading part is arranged on the stock bin, and the grading part comprises a high-level material level sensor which is arranged at the top of the stock bin and used for preventing excessive filling of materials. According to the utility model, the grading part is arranged, and high, middle and low three-level material level monitoring points are arranged in the stock bin, so that the real-time monitoring of the material level in the stock bin is realized, the overflow caused by excessive filling of the material can be effectively prevented, the continuity of material supply is ensured, the interruption of production due to material shortage is avoided, and when the material level drops to an early warning point, the production efficiency is improved. The system can trigger a material supplementing mechanism in time to prevent a production line from being shut down due to material exhaustion, so that the continuity and the stability of a production process are maintained.

The invention relates to the technical field of coating machines, and discloses a multi-drying coating machine which comprises a supporting plate body, a base material roller, a composite drying machine and a discharging roller, the two ends of the supporting plate body are rotationally connected with the base material roller and the discharging roller respectively, the composite drying machine is arranged between the base material roller and the discharging roller, a tension shaft is arranged between the base material roller and the composite drying machine, and the tension shaft is connected with the supporting plate body. A coating mechanism is arranged on one side of the tension shaft and comprises a rotary coating roller, a supporting base and a comma scraper, a coating base material is arranged between the rotary coating roller and the supporting base, the comma scraper is arranged on one side of the rotary coating roller, and ultrasonic vibration modules are arranged on the upper side and the lower side of the comma scraper; the ultrasonic vibration module changes an electromagnetic field around the comma scraper to enable the surface of the comma scraper to generate high-frequency vibration; the high-frequency vibration of the comma scraper not only optimizes the distribution of the coating, but also enhances the interaction between the coating and the base material, and effectively promotes the contact and permeation between coating molecules and the coating base material.

The invention discloses a cable laying guider in a bridge, and relates to the technical field of guiders. According to the cable laying guider in the bridge, the guider assembly can be conveniently installed and fixed in the bridge body and can be adjusted or matched according to the size of the bridge body, meanwhile, blowing cleaning can be conducted on the inner side wall of the bridge body, the installation effect is guaranteed, the applicability is higher, and use is more convenient; when the cable is laid, the cable can sequentially pass through the plurality of guider assemblies, and meanwhile, the cable is guided and limited, so that the use is more convenient and faster, the cable can be prevented from being bent or twisted, the friction between the cable and the bridge body is reduced, the risk of cable damage is reduced, and after the laying is completed, the cable is prevented from being damaged. The telescopic cover can achieve a secondary protection effect on the cable, avoids the influence of dust, water stain, collision, scratch and the like, guarantees the laying quality, can enable the cable laying process to be smoother, reduces the shutdown time caused by clamping stagnation or distortion of the cable, and improves the working efficiency.

The invention discloses a ferromagnetic resonance detection and prevention method for a voltage transformer, and belongs to the field of voltage transformer fault monitoring. Whether ferromagnetic resonance exists or not is judged mainly according to the ground capacitive reactance of the voltage transformer and the excitation inductive reactance under the rated line voltage, and if yes, the following steps are executed: a bus zero-sequence voltage signal of the voltage transformer is collected and dispersed; performing windowing processing on the discrete signal by adopting a Hanning window; performing fast Fourier transform on the windowed signal to obtain spectrum signal amplitude distribution; and determining the ferromagnetic resonance type according to the spectrum signal amplitude distribution. According to the method, ferromagnetic resonance can be accurately identified and classified, so that the detection efficiency is improved, the system fault risk is reduced, and the impact of resonance on power equipment is reduced.

The invention relates to the technical field of ultrasonic welding, in particular to a welding head assembly of a high-stability ultrasonic aluminum wire press welder, which comprises a rack, a chopper, a cutter, a wire releasing mechanism and a flattening mechanism are arranged on the rack, the chopper is configured to be capable of vibrating at a preset frequency, and the cutter is configured to be capable of vibrating at a preset frequency. The welding wire is welded on the welded part; the cutter is configured to be capable of cutting off the welding wire; the wire releasing mechanism is configured to be capable of releasing a welding wire along a preset path; and the flattening mechanism is configured to flatten the welding section of the welding wire before welding. During use, a welding section of a welding wire can be flattened in advance through the flattening mechanism, so that on one hand, the contact area between the welding section of the welding wire and the surface of a welded part during welding is increased, the welding quality is improved, on the other hand, the flattening process of the welding section of the welding wire by a chopper can be omitted, the surface of the welded part is prevented from being damaged, and the welding quality is improved. And the production efficiency can be improved while the welding efficiency is improved.

The invention discloses a back-to-back test platform for an energy storage converter, and relates to the technical field of converter testing, and the platform comprises a data collection module which employs a voltage source converter to access a power grid, and collects different load condition data; the capacitance health prediction module is used for constructing a deep learning model to extract features and calculating a fitness function to predict a capacitance state value; the redundancy control path module is used for constructing a redundancy control path to predict the state of the backup capacitor; and a load balancing module. According to the method, through the CNN-PSO-SVR model, real-time response can be carried out when the state of the capacitor changes, it is ensured that the attenuation state of the capacitor can be rapidly fed back to a control system, it is ensured that the capacitor continuously and stably operates under the complex load condition, power output is compensated in time through a redundant path, and the situation that the whole system is shut down due to the fault of a single element is avoided; through dual-path capacitor health monitoring and Droop-MPC dynamic power distribution, power output can be adjusted in time when early attenuation of the capacitor occurs, and system shutdown caused by a single-point fault is avoided.