105 results about "Reliability optimization" patented technology

The invention provides a power grid energy conservation and loss reduction system based on dynamic reactive compensation and a reliability optimization method, and belongs to the technical field of power grid energy conservation and loss reduction. Comprising a data acquisition module which is used for acquiring real-time electrical parameters of a power grid; the reactive power demand calculation module is in communication connection with the data acquisition module and is used for receiving the real-time electrical parameters of the power grid transmitted by the data acquisition module, and the reactive power demand calculation module is further used for calculating the real-time electrical parameters of the power grid based on an improved compensation demand prediction algorithm. And according to the real-time electrical parameters of the power grid, dynamically calculating a reactive compensation amount and a reactive compensation type, and generating a compensation instruction by combining the reactive compensation amount, the reactive compensation type and a preset threshold value. The beneficial effects are that the compensation instruction is generated according to the reactive compensation type and the preset threshold value, and the compensation execution module switches the compensation device according to the compensation instruction, thereby realizing reactive dynamic adjustment, and enabling the power grid to achieve the effects of energy saving and loss reduction.

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention relates to a permanent magnet synchronous host bearing state detection method and system, and the method comprises the steps: 1, enabling a sharp end of a special measurement rod to directly contact with the surface of a host bearing end cover, and collecting an audio signal and a sound wave signal in the operation of a bearing in real time; secondly, the elevator is made to operate under the three different load working conditions of full load, 50% load and no load, and bearing operation signals under all the working conditions are collected; and step 3, transmitting the collected signals to an intelligent AI analysis platform for preprocessing and feature extraction. According to the invention, the intelligent decision-making system can generate a gradient maintenance scheme considering economy and reliability based on an optimization algorithm of reinforcement learning, effectively prolongs the service life of the bearing, reduces the maintenance cost, guarantees the data safety and traceability through the application of the block chain technology, achieves the collaborative diagnosis and knowledge sharing of cross-brand equipment, and improves the reliability of the bearing. The method has remarkable advantages in the aspects of improving the equipment reliability, optimizing the maintenance strategy, reducing the operation and maintenance cost and the like.

The invention discloses an undercarriage key structure weight reduction optimization design method based on reliability, and the method comprises the steps: carrying out the precise modeling and simulation analysis of key parts of an undercarriage through ANSYS, and achieving the geometric parameterization process through the command stream writing of APDL; the reliability weight reduction optimization design is completed in combination with MATLAB software and the established reliability optimization model, and the purpose of weight reduction is achieved while the reliability and strength requirements are met. According to the method, the blank in the prior art is filled, and a new thought and method are provided for lightweight design of undercarriages and other aircraft key components.

The invention relates to the technical field of power battery echelon utilization, and discloses a decommissioned power battery system reliability optimization design method, which comprises the following steps: acquiring and preprocessing battery operation attenuation data; constructing an attenuation prediction model; performing dynamic clustering and grouping after multi-dimensional consistency evaluation; and generating a strategy and feeding back iterative optimization. The system comprises a data acquisition module, a preprocessing module, an attenuation prediction module, a consistency evaluation module, a dynamic grouping module and a strategy execution and feedback module. By adopting the scheme, the problems are solved, the grouping accuracy and stability are improved, the secondary utilization efficiency is improved, the cycle life is prolonged, the operation reliability is improved, and the maintenance cost is reduced.

The invention discloses a multi-source information sensing system based on radio frequency sensing. The multi-source information sensing system comprises an RFID sensing system, reliability optimization deployment in a strong random environment and vibration signal recovery and reconstruction. The RFID sensing system can be used for collecting original signal data of an engine under the non-contact condition. Secondly, reliability optimization deployment is carried out in a strong random environment, and deployment of system tags, antennas and sensing parameters is optimized under the condition that influences of various interference factors in the working environment of the engine are comprehensively considered, so that the performance of the sensing system is improved. The method can be used for realizing accurate recovery of vibration characteristic signals, ensuring quick and accurate sensing of key parameters of an engine, and realizing quick detection of typical fault modes. The technology has wide application potential and can play an important role in the fields of engine vibration monitoring, engine fault analysis, engine health management and the like.

The invention discloses a multi-sensor fusion electrical equipment control state monitoring method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a first monitoring time period, presetting a data processing time window, obtaining a second monitoring time period, and obtaining a state data sequence; obtaining the ith pre-data weight and the ith trend coefficient corresponding to the jth sensor; the ith pre-data weight of the jth sensor is corrected according to the ith trend coefficient of each sensor, and the ith target data weight of the jth sensor is formed; and obtaining the state weight of the jth sensor according to the plurality of target data weights of the jth sensor, and obtaining the real-time state index of the electrical equipment according to the real-time state data collected by each sensor and the state weight of each sensor. The method has the advantages of reliability optimization, multi-dimensional false alarm suppression and trend collaborative verification.

The invention provides a composite beam reliability optimization method based on a failure probability function. The method comprises the following steps: 1) defining a random input variable, a design variable, a target function and a performance function; 2) calculating a failure sample and an extended failure probability based on an autonomous learning Kriging agent model and a Monte Carlo method according to the Bayesian theory; the method comprises the steps of (1) calculating a design parameter failure sample, (2) calculating a joint probability density implicit function on the basis of an adaptive K-nearest neighbor method and autonomous learning Kriging according to the calculated design parameter failure sample, and (3) calculating a failure probability implicit function on the basis of the Bayesian theorem.By means of the method, the problem that in the reliability optimization process, the calculation cost is difficult to bear due to reliability analysis is solved.

The invention provides an optimization method for predicting the long-term welding reliability of a wire harness terminal and a conductor, and the method comprises the steps: collecting temperature and time data in a welding process, processing the data through a thermal simulation model, and obtaining the change trend of conductor lattice arrangement; according to the change trend of conductor lattice arrangement, the initial thickness and component distribution of oxide layer formation are obtained, and microstructure features of an oxide layer group are determined; aiming at the microstructure characteristics of the oxide layer group, simulating a long-term use environment by adopting an accelerated aging test to obtain a prediction curve of resistance increase; according to the quantitative model of the aging behavior, a performance evolution rule in the actual use environment is obtained, and the incidence relation between welding parameters and long-term reliability is determined; for a process scheme for improving the stability of a conductor, the resistance change of the conductor in a high-temperature environment is simulated and verified, and the accuracy of quantitative correlation is judged.

The invention relates to the technical field of intelligent optimization design, in particular to a bridge prestressed steel beam intelligent optimization design method based on multi-factor coupling, and the method comprises the steps: carrying out the quantitative modeling of a plurality of parameters in the arrangement process of a bridge prestressed steel beam, achieving the precise expression of controllable variables, and improving the design precision of the bridge prestressed steel beam. The overall search capability of the arrangement scheme under multi-target tradeoff is enhanced, the rationality and stability of steel beam distribution are integrally improved, the structure local overload risk is reduced, an energy function sequence is constructed through indexes such as a midspan displacement value, a section stress ratio and the total length, the quality of a solution set is controlled by utilizing an energy difference value, intervention and screening of a residual value are introduced, and the stability of the structure is improved. The robustness of the design under the multivariable condition is enhanced, through logic judgment of displacement and counterforce, unqualified arrangement is eliminated, feasible arrangement output under structural consistency is achieved, structural efficiency and material use economy are enhanced, and design reliability, optimization solution efficiency and structural response control precision are remarkably improved.

The invention discloses a decoupling design method and system for reliability optimization of an angular contact ball bearing. The method comprises the following steps: determining initial design parameters of the angular contact ball bearing; constructing a reliability optimization design model which takes maximization of the rated dynamic load as a target and comprises probability reliability constraint processed by a penalty function; constructing a design variable and failure probability mapping agent model, specifically, identifying and quantifying uncertainty parameters, constructing a quasi-statics model and a limit state function, calculating the failure probability of a sample point through a probability reliability method, and training the agent model taking the design variable as input and the failure probability as output; and based on the proxy model, solving the reliability optimization design model by using an optimization algorithm, and outputting optimal bearing design parameters. According to the method, the reliability analysis process and the optimization iteration process are decoupled, so that the optimization efficiency is greatly improved while the design robustness is ensured.

The invention discloses a hot account efficient processing system and method based on Redis, Flink and timed tasks, a computer and a storage medium. The hot account efficient processing system comprises the steps that hot account early warning information and cache balance are stored based on Redis; summarizing the number of account transactions based on Flink, and pre-warning hotspot accounts; asynchronous entry bottom and balance check are realized based on a timed task, and Flink failure or data loss during issuing is avoided. According to the invention, the system response speed and processing efficiency are improved, the data accuracy and consistency are enhanced, the system stability and reliability are improved, the resource utilization and cost control are optimized, and the risk prevention and control capability is enhanced.

The present application discloses a reliability optimization method, equipment, medium and product for multi-stage peak-shaving tasks of a virtual power plant, which relates to the field of virtual power plant peak-shaving. The method includes constructing a virtual power plant information channel availability model and a virtual power plant information-physical equipment availability model; establishing a binary decision diagram model for all-stage tasks of the virtual power plant; determining the reliability index of all-stage tasks of the virtual power plant based on the above model; establishing and solving a reliability optimization model that considers the uncertainty of real-time peak-shaving service demand and a reliability optimization model that considers the uncertainty of the main scheme equipment state, optimizing the reliability index of all-stage tasks of the virtual power plant, and completing the optimization of the reliability of the multi-stage peak-shaving tasks of the virtual power plant. The present application can take into account the impact of information-physical interaction on the expressiveness of the virtual power plant in the process of participating in the electricity market, and realize the optimization of the reliability of the multi-stage peak-shaving tasks of the virtual power plant.

The application provides an intelligent unmanned system decision reliability evaluation and optimization method based on reinforcement learning, and belongs to the technical field of intelligent unmanned systems. The application adopts a multi-layer operation network model based on a coloring graph theory to represent individual autonomy and functional diversity, overcomes the limitation of a single-layer network, and provides a basis for decision analysis. An intelligent unmanned system distributed decision model based on an enhanced actor-critic architecture is adopted to improve the strategy performance and learning stability of individual decision. An intelligent unmanned system decision reliability evaluation model oriented to an operation loop is adopted. The model integrates the proposed reliability indexes into the individual learning process to improve the effectiveness of evaluation. The model successfully quantifies the influence of individual decision on system reliability. A decision reliability optimization model based on a cooperative multi-agent deep deterministic policy gradient algorithm is adopted. The proposed cooperative reward is integrated into the policy gradient optimization process to optimize the decision reliability.

This invention discloses a PCBA (Printed Circuit Board Assembly) product screening method based on reliability requirements, comprising: assessing the early failure rate of the PCBA product; generating a reliability target value for the product based on the early failure rate and preset conditions; establishing a functional relationship between the reliability target value and the early failure rate, the stress intensity used for screening, and the screening time; and determining the optimal screening stress intensity and screening time in conjunction with the preset conditions. This method helps PCBA manufacturers to accurately determine screening results and costs; ensures that PCBA products maintain a reliable level while controlling screening costs; and helps PCBA manufacturers accurately screen products with potential failures, thereby improving product reliability and optimizing production efficiency.

The application discloses a wheel disc reliability optimization method considering multiple failure mode correlations, and belongs to the field of aerospace. The method comprises three steps: a pair-copula method is used to analyze correlations between failure modes; a reliability distribution method is used to distribute wheel disc reliability to the failure modes; and an improved multi-objective particle swarm optimization algorithm is used to realize wheel disc reliability optimization design. The application establishes a reliability analysis and distribution model based on a cost function from the perspective of system reliability, and realizes a low-cost reliability optimization method considering failure correlations.

The embodiment of the invention discloses a virtual power plant optimal scheduling method and related equipment considering the participation of a multi-type energy storage technology, and the method comprises the steps: building a virtual power plant system model and a multi-type energy storage characteristic analysis model for a virtual power plant, and carrying out the optimal scheduling of the virtual power plant based on the constraints of the virtual power plant system model and the multi-type energy storage characteristic analysis model. And establishing a virtual power plant optimization scheduling model, solving the virtual power plant optimization scheduling model by using an improved dogvessel colony algorithm, and scheduling the virtual power plant according to a solving result. According to the method, collaborative optimization scheduling of the energy resources in the virtual power plant is realized by constructing the optimization scheduling model considering the multi-type energy storage technology and combining the efficient solving algorithm, the defects of an existing virtual power plant scheduling method in the aspect of energy storage application can be overcome, the comprehensive utilization efficiency of the energy resources in the virtual power plant is improved, and the energy resource utilization rate of the virtual power plant is improved. The power supply stability and reliability are enhanced, and the economic benefits of the virtual power plant are optimized. The method is widely applied to the technical field of power system dispatching.

The invention discloses a time-varying reliability optimization design method and system for a helical gear. The method comprises the following steps: establishing a gear reliability optimization design model which takes gear volume minimization as an optimization target and takes full-life-cycle time-varying reliability as a constraint; constructing a gear contact fatigue time-varying limit state function coupling the time-varying load spectrum and the material strength degradation model; constructing a time-varying reliability analysis module based on an agent model and an active learning strategy so as to evaluate the time-varying limit state function; and an optimization algorithm is adopted to solve the gear reliability optimization design model, the time-varying failure probability of the candidate scheme is evaluated by calling the time-varying reliability analysis module, the time-varying reliability constraint is processed according to the time-varying failure probability, and optimal gear design parameters meeting the constraint are output. According to the method, efficient cooperation of time-varying reliability high-precision evaluation and lightweight optimization design is realized, and the problem that a time-varying factor cannot be considered in optimization due to too high analysis cost in a traditional method is solved.

The invention discloses a multi-chip module (MCM) interconnection reliability optimization method based on a WOA-GRNN. The method comprises the steps of MCM three-dimensional automatic modeling, load condition application, interconnection reliability WOA-GRNN modeling, and WOA-GRNN modeling result and analysis. According to the core of the method, an ANSYS parameterized language is adopted to construct a three-dimensional (3D) model and interconnection reliability analysis, a WOA-GRNN technology is adopted to quickly construct a nonlinear model of MCM input and output based on collected data, a reliability database is generated according to the nonlinear model, and key guidance is provided for MCM reliability design and analysis. The MCM interconnection reliability prediction method can greatly improve the MCM interconnection reliability prediction efficiency, further provides important reference for the layout design of the chip, and is an important measure for guaranteeing the MCM chip reliability.

This invention discloses a method and system for predicting the trajectory of electric vehicles at signalized intersections, relating to the field of intelligent transportation technology. The method and system provided by this invention improve the accuracy and efficiency of electric vehicle trajectory prediction in complex intersection scenarios by utilizing Mamba to capture long-term temporal dependencies in signalized intersection videos and incorporating kinematic physical constraints using PINN. By constructing counterfactual scenarios of trajectory feature parameters for counterfactual inference prediction, the causal quantification of key factors affecting trajectory prediction results is achieved, enhancing model interpretability. Furthermore, Bayesian methods are used to perform uncertainty analysis on the Mamba-PINN fusion model, quantifying the uncertainty of trajectory prediction and improving model reliability. The optimized model exhibits strong adaptability and can provide decision-making basis and technical support for traffic signal optimization and intelligent transportation systems.

The invention discloses a fiber reinforced composite material variable stiffness reliability multi-scale optimization design method, and belongs to the technical field of composite material structure reliability optimization design. The method comprises the following steps: constructing a composite material multi-scale reliability topological optimization model considering material and load uncertainty; an improved single-cycle single-vector chaos control (SLSV-MCC) method is provided, and probability constraints are efficiently and accurately processed by adopting a current design point gradient and a stable factor; and in combination with a normal distribution fiber optimization (NDFO) model, parameterizing a discrete fiber angle to realize synchronous optimization of a macroscopic topology and a microcosmic fiber path. According to the method, the problems of low calculation efficiency and unstable convergence when the existing method is faced with multi-scale and strong-nonlinearity problems are effectively solved, and a lightweight composite material design scheme with high reliability in an uncertain environment can be obtained.

The application discloses a kind of comprehensive flexible resource reliability optimization methods considering dynamic multi-energy demand response.Comprehensive flexible resource is divided into four kinds of multi-state comprehensive flexible resource, construct the multi-state model of comprehensive flexible resource considering time sequence characteristics, obtain the multi-energy load demand of each comprehensive flexible resource;Respectively, the multi-state model of comprehensive flexible resource reduction, transfer, alternative response considering time sequence characteristics is established, obtain the multi-energy load demand of comprehensive flexible resource after reduction, transfer, alternative response end in each state;Fusion three kinds of multi-state model, establish the multi-state model of total demand response, obtain the amount of demand side operation reserve provided in multiple states, and then calculate the reliability of the amount of demand side operation reserve provided by comprehensive flexible resource.The application adopts multi-state model to more accurately characterize the dynamic characteristics of comprehensive flexible resource, and accurately obtains the operation risk parameters of the amount of demand side operation reserve provided by comprehensive flexible resource.

The present application relates to a kind of hydraulic system three-dimensional depth double linkage reliability optimization method of thermal power generating unit, comprising the following steps: accounting top shaft oil pressure;Select appropriate position as the introduction point, prefabricate bilateral top shaft oil connecting device to realize system three-dimensional depth extension;Water pressure test is carried out to connecting device to ensure the tightness of shutdown mechanism;Connecting device is purged to ensure the cleanliness of oil system;When unilateral failure occurs, operate shutdown switching connecting device, ensure that top shaft oil hydraulic system is normally put into use.The present application ensures that A, B top shaft oil system is mutual standby through the low-cost reasonable modification of oil supply system, once unilateral failure occurs, it can be ensured that top shaft oil hydraulic system is normally put into use by operating shutdown switching device, and maintenance personnel can have sufficient time to overhaul the fault oil pump and other equipment.

The present application relates to the technical field of embedded system construction, and particularly relates to a software dependency automatic integration method and system based on meta packaging, wherein the method comprises the following steps: S1, creating a meta packaging framework in an embedded construction system, and defining a construction stage hook through a Makefile file; S2, automatically deploying pre-compiled software packages and shared dependent libraries to a target directory of the construction system through a resource injection script; S3, parsing a metadata file of the pre-compiled software packages, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; S4, using a dependency aggregation script to write the global dependency relationship list to a dependency declaration configuration of the meta packaging. Through implementation of the present application, the problems of dependency sequence disorder and metadata audit failure during integration of an embedded system are solved, and the reliability of system integration is improved, the system construction efficiency is optimized, the security of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention discloses a Voronoi type dot matrix reliability optimization method and device, and belongs to the field of structural lightweight design. The method comprises the steps of performing unit division on a to-be-optimized part, and determining the number of seed points in each unit according to a division result; sequentially inputting the number of the seed points in all the units into a pre-trained Voronoi type lattice structure optimization model, and outputting to obtain the optimal distribution of the seed points in each unit; wherein the structure optimization model is established according to unit quality, the number of seed points and a unit bearing failure probability, the unit bearing failure probability is determined according to a preset performance distribution agent model, and the performance distribution agent model is determined according to a finite element simulation result of a Voronoi type lattice geometric model; and according to the optimal distribution of the seed points in each unit, generating an optimal part meeting a preset requirement. According to the Voronoi type lattice structure, the reliability of the Voronoi type lattice structure can be effectively improved.

The invention relates to the technical field of load distribution control, in particular to a power station energy storage power supply load intelligent regulation and control system, which comprises a complementary energy estimation module, a gradient strategy module, a current identification module, a loop control module and a feedback verification module. According to the invention, the residual electric energy of the energy storage unit is accurately evaluated, the real-time dynamic distribution of the load is realized, the real-time monitored electric power parameters are utilized, the setting of the load priority is optimized, the power supply and demand matching precision is improved, the dynamic scheduling path optimization is adopted, and an interference control mechanism is combined, so that the stability and reliability during loop switching are ensured; according to the invention, the system can effectively control the load of the power station, avoids the instability of a load loop and energy waste, further improves the accuracy of a control result through precise feedback verification, records the load control state in real time, enhances the emergency response capability of the power station and the reliability of long-term operation, optimizes the intelligent management of the load of the power station, and improves the operation stability and safety.

The application discloses a degradation fault output voltage ripple optimization method and system for a DC-DC converter, relates to data processing processes such as undersampling, feature extraction and feature selection, a fault diagnosis method based on an extreme learning machine and a reliability optimization method based on DC-DC converter model analysis, belongs to the field of power electronic system fault diagnosis, and comprises the following steps: undersampling technology for the output voltage of a DC-DC converter; time domain feature extraction technology for undersampling data of the output voltage of the DC-DC converter; recursive feature elimination technology based on a support vector machine for selecting the time domain features of the extracted undersampling data of the output voltage of the DC-DC converter; a support vector machine degradation fault diagnosis model based on a Gaussian kernel and a Bayesian optimization for learning multi-class degradation fault data and performing fault classification; and an analysis method based on a physical model of a power conversion device for optimizing the output voltage ripple of the power conversion device.

The invention belongs to the technical field of thermal management and reliability optimization, and particularly discloses a heterogeneous hybrid switch unit thermal reliability optimization method based on a safe operation area, and the method comprises the following steps: S1, obtaining a corresponding two-dimensional safe operation area projection under a rated parameter according to the safe operation area of a heterogeneous hybrid switch unit, extracting a current maximum value Imax20 according to the regional projection; s2, acquiring a current load current IF, comparing the size of Imax20 with the size of IF, if Imax20 is greater than or equal to IF, performing S3, and if Imax20lt, performing S3; when IF, performing S4; s3, single parameter adjustment is carried out; s4, carrying out two-parameter adjustment; and S5, inputting the target optimization parameters into a PWM signal generation module, and generating SiC-based device and Si-based device driving signals corresponding to the target switching frequency and the SiC interruption proportion through cooperative work of the DSP and the FPGA, thereby expanding the safe operation area of the hybrid switch unit. The over-temperature risk of the hybrid switch unit is avoided, and the reliability of the hybrid switch unit is improved.

The invention provides a configuration reliability optimization method, device and equipment for an AC / DC hybrid power distribution network, and relates to the technical field of power distribution networks. The method comprises the following steps: carrying out load type division on loads in a target area, and determining photovoltaic output time sequence characteristics in the target area and load time sequence characteristics of each load type; obtaining an initial configuration scheme of the AC / DC hybrid power distribution network of the target area; taking the initial configuration scheme as an initial solution, and optimizing a preset to-be-optimized item in the initial configuration scheme based on a preset constraint condition to obtain a target configuration scheme of the AC / DC hybrid power distribution network of the target area; wherein in the optimization process, based on photovoltaic output time sequence characteristics and load time sequence characteristics of each load type, a sequential Monte Carlo method is adopted to simulate each configuration scheme and solve a reliability index, and the reliability index is used as a fitness function value. According to the invention, the planning quality and operation benefits of the AC-DC hybrid power distribution network can be improved.

The invention discloses a human-computer interaction task failure evolution path identification method based on a binary decision diagram, and aims to solve the problem that a task failure path in a complex human-computer interaction system is difficult to accurately identify. The method comprises the following steps: constructing a man-machine interaction task event tree and converting the man-machine interaction task event tree into BDD; determining a top event based on the event tree, constructing a fault tree and converting the fault tree into BDD; integrating the event tree BDD and the fault tree BDD to form a unified BDD corresponding to the ET-FT model; and traversing and identifying a failure evolution path based on the integrated BDD. Unified modeling of static logic and dynamic evolution is achieved through BDD, the causal relationship is quantified by combining Shannon decomposition and the ITE method, and the method has the advantages of being accurate in modeling, efficient in analysis and high in expansibility, can be widely applied to the fields of aerospace, medical equipment and the like and provides support for system reliability optimization.