352results about How to "Increased operating life" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention discloses a charging pile dynamic power scheduling method, and relates to the technical field of charging pile power scheduling, and the method specifically comprises the following steps: obtaining node state information of each remaining normal node in real time, and generating a remaining bearing capability index and a load balancing index of each normal node; and constructing a power distribution evaluation model for the generated residual bearing capacity index and the load balancing index of each normal node, generating a power distribution evaluation coefficient of each normal node, analyzing the power distribution evaluation coefficient after generation, and evaluating the power bearing capacity of each normal node. Dividing each normal node into a high-bearing node, a middle-bearing node and a low-bearing node according to an evaluation result; and respectively adopting a corresponding power scheduling strategy according to a division result of each normal node. According to the invention, the problem that the power resource cannot be quickly and efficiently allocated after the fault node exits is solved, and efficient and stable dynamic balanced power scheduling and system operation are realized.

The invention relates to the technical field of offshore wind power operation risk analysis, and discloses an offshore wind power operation risk analysis system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting wind power equipment operation state data and environment data of multiple types of sensors, and carrying out the cleaning and preprocessing of the data; the multi-modal data fusion and feature extraction module is used for extracting and fusing multi-modal features from different data sources; and the real-time risk prediction and intelligent decision module is used for analyzing the operation state of the wind power equipment in real time based on a deep learning model, and performing prediction and alarm when a fault trend is detected. By introducing a self-attention mechanism and a Transform architecture, deep fusion analysis is performed on multi-modal data from wind power equipment, and information of different data sources can be weighted and integrated in a unified feature space, so that the system can adaptively focus on important features, and the analysis precision and fault detection capability of the multi-modal data are remarkably improved.

The invention discloses an intelligent dynamic optimization method and system for the battery temperature of a liquid cooling energy storage cabinet, and particularly relates to the technical field of dynamic optimization of the battery temperature, and the method comprises the steps: obtaining a temperature data set in a battery monitoring region in real time; obtaining a battery operation evaluation coefficient in real time, analyzing the battery operation evaluation coefficient, and determining the operation state of the battery; the operation state of the battery comprises a discharging state and a charging state, and a discharging normal instruction or a charging normal instruction is generated respectively; according to the method, the temperature data set and the battery operation evaluation coefficient are input into the pre-constructed neural network model, the future temperature change can be predicted, the temperature change in the discharging and charging processes is analyzed according to the generated discharging or charging optimization instruction, and the targeted temperature optimization strategy is obtained, so that the battery can be effectively optimized when the battery is discharged or charged. A cooling or current control strategy can be dynamically adjusted according to specific conditions, and the problem of battery overheating or low efficiency is prevented.

The invention relates to an air blower based on magnetic and gas combination axial force balance and a control method of the air blower, and belongs to the technical field of axial force regulation and control of air blowers. An axial air suspension bearing and an axial magnetic bearing are arranged in a main machine of the air blower, the axial magnetic bearing is connected with a magnetic bearing controller, and the magnetic bearing controller controls the axial position of a rotor by controlling the current of the axial magnetic bearing; an axial displacement sensor and a radial displacement sensor are arranged to detect the displacement of the rotor; and the main controller is used for controlling the starting and stopping of the axial magnetic bearing and the main machine based on the axial displacement and the radial displacement of the rotor, and sending out early warning. According to the magnetic and air combined axial force balance device, the axial magnetic bearing is used for providing active control force, the air bearing is used for providing passive support, the stability and reliability of a system are improved, and an important protection effect is achieved on core parts of an air blower.

The invention relates to the technical field of wind resource assessment and wind power plant optimization, and discloses a multi-source information fusion measurement system combining laser radar data and wind resource assessment, and the system comprises a data collection module which is used for collecting laser radar data, synthetic aperture radar data, numerical weather forecast data and ground weather station data; the data preprocessing module is used for carrying out noise filtering, time alignment, space alignment and data calibration on the collected data; the information fusion module is used for dynamically fusing the preprocessed multi-source data and outputting high-precision wind speed and turbulence characteristic distribution; and the wind field modeling and prediction module is used for constructing a dynamic three-dimensional wind field based on the fusion data and performing short-time wind speed prediction. Through multi-source data deep fusion, dynamic wind field modeling and intelligent optimization modules, the comprehensive effects of high-precision wind field evaluation, nonlinear wind speed prediction and fan layout global optimization are achieved.

According to the supercritical CO2 circulation and water electrolysis hydrogen production coupled alcohol-electricity co-production system and method, water in an electrolytic bath is electrolyzed through wind and light abandoned electricity, and oxygen and hydrogen are generated. Oxygen and coal are subjected to pure oxygen gasification in a gasification furnace, and generated gasified gases (mainly including methane CH4, hydrogen H2 and carbon monoxide CO) are subjected to direct-fired supercritical CO2 circulation for power generation. Waste gas exhausted by the turbine is subjected to heat recovery through the heat regenerator, water is condensed in the condenser, and remaining CO2 is fed into the gas tank to be stored. One part of CO2 is pressurized by a compressor and then is fed into the combustion chamber through a heat regenerator, and the other part of CO2 and H2 are catalyzed to synthesize methane. According to the system, wind and light abandoned electricity can be converted into hydrogen energy to be stored, efficient clean power generation of coal and synthesis of green methanol are achieved through direct-fired supercritical CO2 circulation, and the green methanol / electricity co-production system with zero carbon emission is formed.

The invention discloses a roller press control system and method based on online simulation and deep learning, and belongs to the technical field of roller press energy conservation. Intelligent control management is performed on a roller press system through combination of an online monitoring technology, a CAE dynamic simulation technology and a deep learning technology; the stable operation of the roller press is guaranteed, the production efficiency and the service life of the roller press are improved, the grinding energy consumption is reduced, and the management level is improved. In addition, the invention provides a set of complete roller press capacity and service life optimization management control system and method, which integrates the advantages of real-time and high efficiency of online monitoring, the advantage of microcosmic particle dynamics accurately captured by a CAE dynamic simulation technology, and the advantage of rapid response of a deep learning technology. And the actual complex and changeable working conditions of the roller press are quickly calculated, and instant response control is realized.

The invention discloses an industrial Internet of Things edge computing unloading method and system based on a distributed deep neural network, and the method comprises the following steps: firstly, introducing a feature matrix of an attention mechanism optimization task, transmitting the optimized task features to a multi-layer deep neural network DNN, and generating an unloading decision set in parallel; allocating resources for each task; after the to-be-unloaded task is preliminarily selected, computing resources of the high-priority task are preferentially guaranteed in the resource allocation stage according to the priority and deadline of the task. For tasks with relatively large calculation amount or relatively high priorities, decision storage is performed by using a cache strategy to optimize unloading and resource allocation processes of subsequent similar tasks, so that efficient and low-delay task unloading in a large-scale and multi-task environment is realized. Meanwhile, a distributed cache updating mechanism is utilized in each unloading time slot, the unloading decision precision is continuously optimized, and the unloading efficiency and the overall stability of the system are improved.

The invention discloses a spacecraft executing mechanism fault diagnosis and online calibration device and method based on double sensors, and belongs to the technical field of aircraft control and ground simulation. In order to carry out detection and off-line compensation on errors, the device comprises an absolute grating encoder, an actuating mechanism expansion shaft, an inductosyn, a control moment gyroscope, a photoelectric reading head and a signal amplification circuit, the absolute grating encoder, the inductosyn and the control moment gyroscope are coaxially installed through the executing mechanism expansion shaft, one end of the executing mechanism expansion shaft is connected with the absolute grating encoder, the other end of the executing mechanism expansion shaft is connected with the inductosyn, and the inductosyn is connected with the control moment gyroscope. The absolute grating encoder is connected with a first processing circuit box through a photoelectric reading head wire, and the first processing circuit box is connected with an upper computer. The inductosyn is connected with the signal amplification circuit and the excitation power supply through wires, the signal amplification circuit is connected with the second processing circuit box through a wire, and the second processing circuit box is connected with the upper computer.

The invention relates to the technical field of generator set fault detection, in particular to a nuclear power emergency diesel generator set health detection method. Vibration signals and thermal parameters in the working process of the diesel generating set are collected through an external sensing device; filtering the collected vibration signals and establishing a dynamic data set; establishing a kinetic model of the diesel generating set according to the vibration signals in the dynamic data set, and performing anomaly analysis according to the established kinetic model and the thermal parameters to obtain abnormal vibration signals; and evaluating the health degree of the diesel generating set according to the abnormal vibration signal. The invention further provides a nuclear power emergency diesel generating set health detection system, the nuclear power emergency diesel generating set health detection method is adopted to achieve real-time monitoring and early warning of the diesel generating set, and the operation efficiency and safety of the emergency diesel generating set are effectively improved.

The invention discloses a method of an elevator energy consumption optimization control system, which relates to the technical field of elevator energy consumption optimization, and comprises the following steps: in the elevator running process, acquiring elevator running information of a floor planned to stop in the running process in real time, and preprocessing the acquired elevator running data to obtain a pre-processed elevator running data; and a basis is provided for analyzing the use mode and the operation efficiency of the elevator. According to the invention, through intelligent division of near-distance stopping, middle-distance stopping and long-distance stopping, the elevator system realizes fine speed and acceleration adjustment aiming at different running distances, energy consumption and transportation efficiency are effectively balanced, and the power peak demand and electric power waste are reduced. The instantaneous current impact is reduced by short-distance stopping, and the mechanical wear is relieved; and the acceleration is optimized by long-distance parking, so that excessive consumption under high load is prevented, and the temperature rise of the equipment is avoided. On the whole, intelligent control reduces equipment aging and heat dissipation requirements, the service life of the elevator is remarkably prolonged, and the long-term maintenance cost is reduced.

The invention discloses a method for predicting the service life of a proton exchange membrane fuel cell based on deep learning. The method comprises the following steps: carrying out noise reduction processing on proton exchange membrane fuel cell data by using discrete wavelet transform, and extracting feature data; the characteristic data are input into a trained deep learning model, predicted voltage data of the proton exchange membrane fuel cell are obtained, then the remaining useful life of the cell is determined based on the predicted voltage data, and the deep learning model is constructed based on a Non-station Transformer model. The method can effectively improve the prediction precision of the remaining useful life of the cell, predicts the attenuation state of the proton exchange membrane fuel cell, and facilitates the design of a related energy management strategy to prolong the service life of the cell.

The invention provides a device and a method for drawing inorganic crystal and amorphous materials by using a graphite heater, and belongs to the technical field of nano materials. The technical problems in the drawing process of inorganic crystal materials and inorganic non-crystal materials are solved. Inorganic crystal materials and inorganic non-crystal materials are drawn by using a method different from a CO2 laser for the first time. The drawing process comprises the following steps: (1) designing a soft pulling force parameter, and additionally arranging an adjustable soft pulling force in a heating process, so that the shape and the size of a drawn product have a more flexible adjusting space; and (2) a drawing process control mechanism of one of'displacement ', 'heating time' and'speed 'is adopted, so that the requirements of preparing different micro-nano probes and ultramicro electrodes are met.

The invention discloses a visual quantitative measurement and analysis device and method for detecting the surface discharge degree of an insulating material, and relates to the field of visual quantitative detection of the surface discharge degree of the insulating material. By constructing an electrostatic discharge system, the pulse discharge process of the insulating surface under the actual operation condition is simulated, and then the surface of the insulating material is induced to discharge. A visual processing device is built, through the polarization effect between dust and charges, the surface charge distribution condition is rapidly reproduced through the dust, and equipment recording is shot. A gray-scale map working principle is applied, a gray-scale conversion and binarization processing program is written, the program is started to convert a recorded picture into a binary image only containing black and white pixel points, the number of the pixel points is counted, and the charge distribution density in an effective discharge radius is calculated through formula conversion, so that the electrostatic discharge degree is quantitatively represented. By changing environmental conditions such as humidity and air pressure, service life state evaluation of the insulation equipment and insulation detection and structure optimization work of the novel insulation equipment in a complex environment are researched.

The invention relates to an energy storage optimal configuration technology, in particular to an energy storage optimal configuration method and system considering endogenous and endogenous dual uncertainty, and the method comprises the steps: taking the sum of the minimum energy storage configuration cost, the minimum energy storage loss cost and the minimum system scheduling cost as a target function, and building an energy storage configuration optimization model; determining constraint conditions; an optimal solution of a decision-making dependency uncertainty robust problem is realized through a dual column constraint generation method, and an energy storage planning decision-making scheme is obtained through two-stage transformation and CPLEX solver calling. According to the method, the exogenous uncertainty of new energy and the endogenous uncertainty of energy storage are comprehensively considered, the energy storage life attenuation characteristic is considered, the energy storage planning configuration method is constructed, and the energy storage operation life is guaranteed while the system dispatching economy and the energy storage planning economy are improved. The problems that a traditional column constraint generation algorithm cannot adapt to the worst working condition considering the endogenous uncertainty problem and is difficult to determine are solved, and the economic dispatching level of the system is optimized.

The invention discloses a moving contact applying a comb-shaped contact finger structure and a clamp type disconnecting switch, relates to the technical field of disconnecting switches, and is used for solving the problem that the existing clamp type disconnecting switch is concentrated due to clamping force or stress. The moving contact comprises a push plate, supporting pieces, a contact finger, a clamping spring and a mounting plate, the end part of the push plate is connected with one end of the supporting piece on the corresponding side through a connecting plate, the other end of the supporting piece is hinged with the mounting plate, one end of the supporting bent plate is hinged with the mounting plate, and the other end of the supporting bent plate is hinged with the clamping spring. The clamping spring is located between the supporting piece and the other end of the supporting bent plate. The two groups of contact fingers are symmetrically arranged between the two supporting bent plates, one end of each contact finger is fixedly connected with the supporting bent plate on the corresponding side, and the other end of each contact finger is in a comb tooth shape and is used for being in contact with a static contact; one end of the pressing plate is fixedly connected with the other end of the supporting bent plate, and the other end of the pressing plate is contacted with the other end of the contact finger from the inner side. Reliable clamping force exists between the clamping device and the static contact.

The invention provides a grinding, spraying and rolling composite surface machining method for a gear tooth surface, which comprises the following steps of: determining the overall maximum elastic deformation of a gear to obtain a modified theoretical involute gear tooth profile; by means of the micro-line-segment theory, a micro-line-segment type involute gear tooth profile is obtained; by means of non-linear smooth fitting, a final modified involute gear tooth profile is obtained, and an initial gear is obtained through machining; on the basis of the initial gear, a gear shot blasting impact model is built, and a shot blasting gear is obtained; and on the basis of the shot blasting gear, a gear finishing and grinding model under gear tumbling finishing machining is built, and a finished gear is obtained. Grinding, shot blasting and barreling finishing are combined, through process coupling design, the overall performance of the gear is improved, higher manufacturing precision is achieved, the failure risk is reduced, the service life of the gear is prolonged, and the gear has the practical application value.

The invention discloses a three-degree-of-freedom macro-micro composite piezoelectric nano probe station, and relates to the technical field of semiconductor manufacturing and biotechnology. In order to solve the technical problems that in the prior art, an existing motor-driven probe station is large in cooperative control difficulty, the positioning precision and the stability are difficult to consider, the structure is complex, and the energy consumption is high, the technical scheme provided by the invention is that the three-degree-of-freedom macro-micro composite piezoelectric nano probe station is provided. Comprising a probe bench surface, an XY macro-motion mechanism, an XY micro-motion mechanism and a Z-direction mechanism. The XY macro-motion mechanism comprises an X piezoelectric motor and a Y piezoelectric motor which are used for achieving dozens of millimeter level horizontal movement in the X-axis direction and the Y-axis direction respectively. The XY micro-motion mechanism is used for realizing accurate movement of several nanometers to dozens of nanometers in the X-axis direction and the Y-axis direction; the Z-direction mechanism is used for realizing dozens of microns of lifting in the Z-axis direction; the probe bench surface is used for bearing a bearing object and is also used for driving the bearing object to move along the X-axis and Y-axis directions. The device is suitable for semiconductor manufacturing and biotechnology testing work.

The invention relates to a full-process automatic operation control method for a sub-medium-pressure steam turbine island. The method comprises the following steps of (1) starting inspection, (2) auxiliary engine starting, (3) speed increasing, (4) grid connection, (5) load increasing, (6) automatic shutdown and (7) bypass automatic operation under abnormal working conditions. According to the scheme, operation of operators is reduced, and the automation level of the unit is improved. The invention further provides a control method for economical and safe operation of the steam turbine based on boundary control.

The invention relates to the technical field of power generation and energy storage, and discloses a user-side energy storage oriented robustness strategy optimization method, which comprises a robustness strategy optimization system, and the robustness strategy optimization system comprises a user-side energy storage facility digital twinborn model, a user-side energy storage facility digital twinborn model, a user-side energy storage facility digital twinborn model and a user-side energy storage facility digital twinborn model. The user side energy storage facility digital twinborn model comprises a real-time data acquisition module, a data processing module, a digital twinborn model construction module, a strategy optimization module, a user interaction module, an energy storage control module and an energy storage equipment state monitoring module. By integrating a real-time data acquisition module, a data processing module, a digital twin model construction module, a strategy optimization module, a user interaction module, an energy storage control module and an energy storage equipment state monitoring module, the running state of the energy storage equipment can be monitored and adjusted in real time, so that the running efficiency of the energy storage equipment is improved, and the service life of the energy storage equipment is prolonged; and through real-time monitoring and optimization strategies, safe and stable operation of the energy storage method is ensured, and a comprehensive solution is provided for management of the user side energy storage equipment.

The invention provides a thermal management control method for an energy storage system. The thermal management control method comprises the following steps: acquiring information of a battery system; acquiring information of the liquid cooling system; judging whether the communication between the battery system and a control system is normal or not, and if yes, adopting a first control mode: controlling the operation of the liquid cooling system based on the information of the battery system and the information of the liquid cooling system at the same time; if not, a second control mode is adopted, and operation of the liquid cooling system is controlled only through the information of the liquid cooling system. The operation of the liquid cooling system is controlled based on the operation information of the battery system and the operation information of the liquid cooling system at the same time, the regulation and control efficiency is higher, the operation duration of the liquid cooling system can be shortened, and the operation life of the system is greatly prolonged.

The utility model discloses a mounting structure for an air cooling system of a dry-type transformer. The mounting structure comprises a high-voltage coil, a low-voltage coil and an iron core, a high-low-voltage coil air duct is reserved between the high-voltage coil and the low-voltage coil; an air channel between the iron core and the low-voltage coil is reserved between the low-voltage coil and the iron core; a plurality of fans are mounted on the base at the bottom of the coil and are symmetrically distributed on two sides of the high-voltage coil; and each fan is mounted on the corresponding triangular bracket, so that the fans mounted on the triangular brackets are inclined upwards, and cooling airflow generated by the fans flows towards the air duct between the high-voltage coil and the low-voltage coil and the air duct between the iron core and the low-voltage coil. After the air cooling system mounting structure is adopted, the air flow passing through the air channel between the high-voltage coil and the low-voltage coil of the transformer and the iron core can be changed by adjusting the air blowing position and the air blowing angle of the fan, the heat dissipation effect is improved, the phenomenon of uneven heat dissipation of the transformer is eliminated, and therefore the heat dissipation effect of the transformer is improved, and safe and reliable operation of the transformer is guaranteed.

The utility model discloses an additional fixing device for laying a glass wool template on a surface layer of a reducing rectangular air duct, which comprises an air duct reducing pipe, additional steel belts are arranged on four surfaces of the air duct reducing pipe at intervals, and each additional steel belt comprises an additional steel belt bottom surface and an additional steel belt fixing side end. The two additional steel belt fixing edge ends are symmetrically arranged at the two ends of the additional steel belt bottom face and form 90-degree angles with the additional steel belt bottom face respectively, locking holes are formed in the additional steel belt fixing edge ends, a plurality of fixing nails are evenly arranged on the additional steel belt bottom face at intervals, glass wool boards are laid on the four surfaces of the air pipe reducing pipe, and the glass wool boards are arranged on the four surfaces of the air pipe reducing pipe. The glass wool board vertically penetrates through the fixing nail, and a hook pressing sheet is arranged at the upper end of the glass wool board and is connected to the fixing nail in a sleeving manner. The glass wool board is fixed through the additional steel belts and the fixing nails, the surface layer structure of the body cannot be damaged, the construction efficiency is improved, the service life is prolonged, and construction is not limited by environment temperature.

The invention relates to the field of service life prolonging control, and discloses a reinforcement learning-based self-adaptive service life prolonging control method for a wind turbine generator, which is used for providing a reliable technical means for prolonging the service life of the wind turbine generator. Comprising the steps of collecting a unit operation state and key component health parameters, and generating multi-dimensional state information; establishing a damage evolution model, predicting the residual life and generating a health degree index; designing a multi-objective optimization strategy, and solving to generate a cooperative control instruction; after the instruction is executed, the model and the strategy are dynamically updated based on unit state feedback, and a closed-loop control system is formed. The service life of the wind turbine generator is effectively prolonged.

The embodiment of the invention discloses a fuel cell system fault processing method and device, and the method comprises the steps: carrying out the AC impedance spectrum test of a fuel cell, obtaining an AC impedance spectrum, building an equivalent circuit model for the reaction process of the fuel cell, carrying out the fitting of the equivalent circuit model according to the AC impedance spectrum, and obtaining the fault of the fuel cell system. Obtaining impedance data corresponding to each reaction process of the fuel cell; determining residual data between the impedance data corresponding to each reaction process of the fuel cell and the corresponding theoretical data; determining a data change trend of the impedance data according to the residual data; and performing fault diagnosis according to the data change trend to obtain a fault diagnosis result of the fuel cell. According to the fuel cell system fault processing method provided by the invention, the fault of the fuel cell can be diagnosed timely, accurately and comprehensively, the reliability and durability of the fuel cell system are improved, and the service life of the fuel cell is prolonged.

The invention relates to the technical field of thermal management of electric vehicles, in particular to a multi-source heat pump control method and system for thermal management of a pure electric vehicle and a medium, and the method comprises the following steps: selecting a corresponding multi-source heat pump control mode according to heating or refrigerating requests sent by a passenger compartment and a battery in combination with vehicle conditions, vehicle speed and environment temperature; measuring the surface temperature T7 of the warm air core; the difference value delta T7 between the surface temperature T7 of the warm air core and the target temperature T7m of the warm air core is calculated, and PI adjustment is conducted on the rotating speed of a compressor according to the difference value delta T7; by adjusting the opening degree of the three-way valve D, the heating requirement of the battery or the passenger compartment is met. According to the actual running state of the vehicle, multiple heat sources can be selected in good time, electric drive waste heat is fully utilized, and energy waste is avoided; the electric automobile can work in the field of the extremely low temperature of-40 DEG C, and the cruising ability of the electric automobile in winter and the comfort level of passengers are guaranteed. Therefore, the technical problems that an existing heat management system is low in heating efficiency and insufficient in heating capacity can be solved.

The invention discloses a component reliability assessment method and system based on big data analysis, and relates to the technical field of component reliability assessment, and the method comprises the steps: collecting multi-source data through installing a plurality of sensors, and carrying out the preprocessing of the multi-source data; performing feature extraction and dimension reduction processing on the preprocessed multi-source data, and constructing an LSTM model to train feature data; inputting multi-source data collected in real time into the trained LSTM model, and dynamically evaluating the reliability of the component; an early warning mechanism is introduced, anomaly detection and early warning are carried out according to the reliability result of the component, and an early warning signal is sent out; and formulating an optimization strategy according to the real-time data and the early warning signal, and executing optimization adjustment. By means of model training, real-time evaluation, an early warning mechanism, dynamic optimization and the like, the accuracy and the real-time performance of component reliability evaluation are remarkably improved, and continuous optimization of system performance is achieved.

The invention discloses a real-time control method for a fuel cell gas supply system based on an MODDPG algorithm. The real-time control method comprises the following steps: step 1, establishing a model of the fuel cell gas supply system; step 2, acquiring actual state information of the fuel cell gas supply system; 3, performing real-time control on the fuel cell gas supply system based on an MODDPG algorithm; according to the method, the MODDPG algorithm fusing the Pareto theory and the DDPG algorithm is provided, and the problem that global optimization is difficult to achieve in complex multi-objective optimization of a traditional discrete action control strategy is solved. Through a multi-target tradeoff mechanism of the algorithm, the optimization requirement of the fuel cell gas supply system in a complex dynamic environment can be better adapted, and the control strategy realizes optimal balance among different targets, so that the applicability and robustness of the control method are enhanced, and the overall efficiency and stability of system operation are improved.