85results about How to "High solution accuracy" patented technology

The invention discloses an inertial microsystem thermal coupling analysis method based on a physical information neural network, and the method comprises the steps: S1, setting material parameters and boundary conditions of an inertial microsystem, and building a thermal coupling analysis model; s2, performing electrothermal coupling analysis to obtain temperature distribution of the inertial microsystem; s3, thermal stress distribution of the microsystem is obtained through thermal-mechanical coupling simulation analysis; s4, predicting a temperature field of the microsystem through the physical information neural network; s5, taking the temperature field as a boundary condition of mechanical simulation of the microsystem to obtain mechanical properties such as stress and strain of the microsystem; and S6, performing mechanical-electric coupling simulation analysis, and analyzing the influence of structural deformation on each parameter in electrical performance. According to the method, the neural network solving precision is improved through the improved adaptive weight strategy, the complete polynomial basis function is combined with the neural network, the state dimension is reduced by introducing the expansion basis function, the calculation cost and time are reduced, accurate prediction of the micro-system multi-moment temperature field is achieved, and the micro-system performance under electric heating power multi-physics field coupling is calculated.

The invention discloses an electric vehicle charging station layout optimization configuration method and system, and the method comprises the following steps: obtaining the traffic network information and power distribution network information of a planning target area, and determining the charging demand number of electric vehicles and the charging load demand distribution condition; according to the electric vehicle charging load demand distribution condition, determining the service range of each charging station, and constructing a proposed charging station capacity determination model; an upper-layer charging station site selection model is constructed, and the optimal site of each charging station is determined; establishing a lower-layer mixed integer programming model, and determining the configuration number of various charging piles of each charging station; solving the double-layer locating and sizing planning configuration model by using a hybrid genetic simulated annealing algorithm and an analytic hierarchy process to obtain an optimal locating and sizing scheme of the charging station; according to the method, the attraction of the charging station to the user and different types of charging pile configuration schemes are comprehensively considered, a bilevel planning configuration model is established, an optimal solution set is solved by using a hybrid genetic simulated annealing algorithm, and finally an optimal scheme is determined through an analytic hierarchy process.

The invention provides a mold surface repairing method and system based on laser cladding, and relates to the technical field of laser cladding, the method comprises the following steps: selecting a matrix material with a proper size, and pretreating the surface of the matrix material; according to the actual process requirements, the value range of laser cladding process parameters is determined, and a response curved surface model of the microhardness and the dilution rate is established; optimizing the response curved surface model, and obtaining an optimized process parameter combination by taking maximization of microhardness and minimization of dilution rate as targets; according to the optimized technological parameter combination, the laser cladding process is carried out, and a cladding layer is formed on the pretreated base material; and performing performance detection on the formed cladding layer, and adjusting process parameters according to a performance detection result until an optimal process parameter combination is determined. By means of the method, it can be guaranteed that the hardness of the cladding layer is stabilized within the ideal range, the mold repairing quality is remarkably improved, and the service life of the mold is remarkably prolonged.

The invention provides a stratosphere aircraft cross-day and cross-night task trajectory planning method and system in a dynamic wind field environment. The method comprises the steps that a task target and task constraints are acquired; acquiring external wind field information; constructing a motion model and a cost function of the stratosphere aircraft in the dynamic wind field; and outputting the lowest feasibility requirement of the cross-day and cross-night task under the dynamic wind field condition and the optimal trajectory when the task is completed by using a level set method in combination with particle swarm optimization. The method has the advantages that a combination framework of the level set algorithm and the particle swarm algorithm is innovatively provided, the problem that the level set algorithm has strong dependence on initial conditions is solved, meanwhile, the search space dimension is reduced, and the probability that the algorithm falls into a local optimal solution is reduced; by flexibly designing a fitness function, conditions such as speed constraint, energy consumption constraint and task feasibility of a stratosphere aircraft cross-day and cross-night arrival or residence task can be comprehensively considered, and the globality and constraint compatibility of trajectory optimization are realized.

The invention discloses a deep learning-based pipeline structure stress proxy model construction method, which belongs to the field of engineering mechanics, energy transmission and artificial intelligence, and comprises the following steps of: firstly, establishing a numerical model of a pipeline, simulating different environmental conditions by applying different loads, and solving to generate a large amount of simulation data; then, feature unit extraction is carried out on the simulation data so as to compress feature dimensions; then, a deep learning initial proxy model of pipeline stress is constructed, and initial training is carried out through finite element simulation data; and finally, transfer learning is carried out based on the initial agent model, the transfer learning is divided into two stages, enhanced actual measurement sample data is used for training in the pre-training stage, and accurate actual measurement samples are used for further training and fine adjustment in the strengthening stage, so that the generalization ability of the model is improved. According to the method, the stress distribution can be rapidly and accurately solved by constructing the stress agent model, the generalization ability is high, and the problem that a traditional physical modeling method is insufficient in real-time performance and precision in stress monitoring and prediction can be solved.

The invention discloses an on-line estimation method and system suitable for cluster type intelligent electric meter error analysis, and the method comprises the steps: building an intelligent electric meter error on-line calculation model through an energy conservation principle based on the characteristic that most intelligent electric meters are distributed according to a tree topology structure in reality, and solving the model to obtain an error estimation value. On this basis, non-uniformity of error information contained in measurement data in time is considered, a weight coefficient represented by attenuation factors is introduced to construct a loss function of a calculation model, different attenuation factors are adopted according to the condition number of a coefficient matrix to improve the tracking capability of an algorithm, an error estimation value is obtained by deriving the loss function, and the tracking precision of the algorithm is improved. And the error estimation value of the electric energy meter is updated in real time through newly collected data, so that the solving precision of the error estimation value is improved.

The invention discloses a calculation method for a nuclear reactor neutron transport equation, and belongs to the field of nuclear reactor core design and safety. According to the method, the problem that the convergence speed of an inner iteration process of a transport equation and a low-order equation in a traditional PI-CMFD method is low is solved. The method specifically comprises the following steps: step 1, reading a coarse mesh model, a fine mesh model, material information and boundary conditions of a nuclear reactor; step 2, establishing a numerical discrete model of a steady-state multi-group transport equation according to the fine grid model read in the step 1; 3, after boundary constraint and material constraint are applied to the established numerical discrete model of the steady-state multi-group transport equation, a discrete equation set is constructed by transforming a test function in the model; and 4, solving the constructed discrete equation set based on the grid mapping relation between the coarse grid model and the fine grid model by adopting a PI method and a CMFD method to obtain a neutron angular flux value and a characteristic value of the neutron transport equation. The method can be applied to calculation of the neutron transport equation of the nuclear reactor.

The invention relates to a special assembly workshop machine and AGV combined scheduling optimization method considering optimal path selection, and the method comprises the following steps: initializing configuration parameters according to a workshop assembly process; a general assembly workshop machine and AGV scheduling scheme is taken as a solution, and a machine and AGV combined scheduling multi-target optimization model considering an AGV transportation optimal path is established; designing a multi-objective learning type artificial bee colony algorithm; and optimizing the scheduling scheme through iterative computation, and finally obtaining a plurality of optimized scheduling schemes which accord with actual production. Compared with a traditional scheduling mode, the method considers the selection of the optimal path during AGV transportation, greatly improves the scheduling efficiency and accuracy of the special assembly workshop, reduces the scheduling time cost and energy cost while providing a reasonable and effective scheduling scheme for workshop scheduling, and improves the scheduling efficiency. The overall production efficiency of an assembly workshop is improved, the order delay condition is reduced, and consumption reduction and efficiency improvement of enterprises are achieved.

The invention discloses a micro-energy network shared energy storage configuration method and device and a storage medium. The method comprises the following steps: acquiring basic data of a micro-energy network and a shared energy storage system; constructing a double-layer optimization model based on the basic data, wherein the double-layer optimization model comprises an inner-layer optimization model and an outer-layer optimization model; according to the inner layer optimization model, the inner layer optimization problem is determined to minimize the annual operation cost under the given shared energy storage configuration; according to the outer-layer optimization model, determining that an outer-layer optimization problem is maximization of the consumption rate of photovoltaic power generation; and solving the double-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme. According to the embodiment of the invention, the double-layer optimization model is constructed to optimize the operation of the micro energy grid and the shared energy storage system, the photovoltaic consumption rate can be effectively improved, the total cost can be reduced, a better energy storage configuration optimization scheme can be solved, and the energy storage optimization effect realized based on the energy storage configuration optimization scheme can be effectively improved.

The invention relates to an optimization design method of a multi-winding regulation and control transformer for flexible interconnection, which comprises the following steps: 1, according to a single-target optimization design mathematical model, constructing an optimization target function with the purpose of minimizing the sum of the material cost of the multi-winding transformer and the ten-year operation cost; 2, determining required constraint conditions according to basic electrical parameters of the multi-winding regulation and control transformer for flexible interconnection; under the action of the obtained optimization objective function, punishing a trend violating constraints by adopting a punishment function, so that non-constraint optimization is gradually close to a feasible region, a constraint optimization problem of the distribution transformer is adjusted into an unconstraint optimization problem, and an optimization design model of the amplitude-phase adjustment type multi-winding transformer for flexible interconnection is obtained; and 3, solving the optimal design model, obtained in the step 2, of the multi-winding regulation and control transformer for flexible interconnection by using a particle swarm optimization algorithm. According to the method, the local optimal solution and the global optimal solution of the individual can be solved, and higher solution precision can be obtained.

The invention discloses a permanent magnet synchronous motor speed regulation control method based on an improved hawk algorithm. The method comprises the following steps: acquiring permanent magnet synchronous motor data; inputting the permanent magnet synchronous motor data into a pre-trained permanent magnet synchronous motor speed regulation control model, and outputting a speed regulation result of the permanent magnet synchronous motor; and the permanent magnet synchronous motor speed regulation control model is trained by using an improved Tianying algorithm. According to the method, the problem of controller parameter adjustment in a permanent magnet synchronous motor speed regulation control model is solved, the improved hawk optimization algorithm is introduced, the algorithm realizes optimal configuration of global search and local search capabilities by virtue of the characteristics of rapid convergence, high global search capability and high solving precision, and the optimal configuration of the global search and local search capabilities is realized in the parameter optimization process. A dynamic simplex method strategy and an annealing simulation-gold sine updating strategy are utilized to expand the optimal selection range, the algorithm shows excellent performance, and the labor intensity of researchers for adjusting parameters is remarkably relieved.

The invention provides an active power distribution network strategy reconstruction method based on an improved grey wolf algorithm, and the method comprises the following steps: stage 1, reading the electrical data of a power distribution network, confirming the switching state and the access position and constraint conditions of a distributed power supply, and carrying out the reconstruction of a power distribution network strategy on the premise of meeting the power flow constraint, operation constraint and topological structure constraint of the power distribution network; and constructing a mathematical model for static reconstruction of the multi-target active power distribution network. Stage 2: then improving the GWO algorithm, the improved strategy including using improved Tent chaotic mapping to perform population initialization, improving a convergence factor and an adaptive t distribution disturbance variation position updating strategy, and obtaining a TGWO algorithm, according to the method, improved Tent chaotic mapping is introduced to carry out population initialization, an attenuation factor updates an alpha wolf position, and a disturbance variation strategy of adaptive t distribution is improved. The improved grey wolf algorithm is improved in operation speed, solving precision and robustness.

The invention belongs to the technical field of steering pull rod design, and particularly relates to a steering pull rod mechanism design method based on analytic geometry and a steering pull rod mechanism. The steering pull rod mechanism is simplified into a plane connecting rod mechanism, and four key hinge points are marked; establishing a coordinate system by taking P1 as an original point; the length, the initial angle and the P4 coordinate of the front steering swing arm are obtained; calculating two coordinates of a P2 point when the front steering swing arm rotates to a limit position; generating P2 ''and P2'' ''points by rotating P2 'P4 and P2'' P4 line segments; and connecting the line segments and constructing perpendicular bisectors, and determining the position of a P3 point by solving the intersection point of the perpendicular bisectors. According to the method, the steering pull rod mechanism is designed through analytic geometry and coordinate transformation, the accuracy of hinge point positioning is improved, and structural deviation caused by empirical estimation is reduced; the design complexity is reduced through standardization steps, the development period is shortened, the motion coordination of the steering system is optimized, and the method is suitable for the field of automobile chassis and the like needing high-precision motion control.

The embodiment of the invention provides a power system robust optimization method and device considering meteorological uncertainty. The method comprises the steps of obtaining historical wind and light output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of a power system; carrying out serialization processing on system elements of the power system to obtain a serialization processing result; based on the serialization processing result and the equivalent reliability index of the electric power equivalent system, determining the credible capacity of the electric power system; performing point prediction on the wind and light output and the load power to correspondingly obtain a wind and light output prediction result and a load prediction result; fitting the wind and light output prediction result and the load prediction result to correspondingly obtain an output prediction power interval and a load prediction power interval; constructing a day-ahead scheduling planning model; performing correction processing on the day-ahead scheduling planning model to obtain an intra-day scheduling optimization model; and solving the day-ahead scheduling planning model and the intra-day scheduling optimization model to obtain a power optimization strategy of the power system.

The invention discloses an electromagnetic wave rain attenuation prediction method based on nonlinear regression, and belongs to the technical field of communication. The method is based on a classical ITU-R rain attenuation model, and comprises the following steps: constructing a training and testing data set for fitting model parameters k and b by collecting rainfall rate and attenuation data; a nonlinear regression method in deep learning is adopted, a model containing k and b parameters is designed and trained, a mean square error is used as a loss function, and parameter solving is carried out through a gradient descent optimization algorithm; and when the similarity between the fitting result on the test set and the real attenuation value reaches a specified threshold value, stopping training and outputting an optimal parameter. According to the method, automatic solving of the rain attenuation model parameters is realized, human intervention is reduced, the calculation precision and the generalization ability of the model are improved, and the method is suitable for electromagnetic wave propagation research in various rain attenuation scenes.

The invention discloses an electric power system real-time low-carbon economic dispatching method and device based on a parallel distributed flexible action device-evaluator, electronic equipment and a storage medium, and the method comprises the steps: jointly participating in the real-time optimal dispatching information of an electric power system according to large-scale new energy and a carbon transaction mechanism; and constructing a real-time low-carbon economic dispatching model of the power system based on the Markov decision process. According to the model, a parallel distributed flexible action device-evaluator algorithm is proposed to train an agent offline to learn a low-carbon economic dispatching strategy; and integrating the trained intelligent agent into a power system dispatching device for application, and deciding a dispatching scheme of each generator set according to the real-time state information of the system. According to the method, on the basis of the real-time low-carbon economic dispatching requirement of a power system, the continuous action space of the dispatching problem is processed through a flexible action device-evaluator; and a distributed flexible strategy iteration framework and a parallel technology are utilized to improve a flexible action device-evaluator, so that the solving speed and precision are improved.

The invention belongs to the technical field of power system dispatching, and discloses a constraint learning embedded optimization method and system for an AC security constraint unit committee, and the method comprises the steps: constructing a mixed integer nonlinear model of an AC power flow-considered SCUC problem of a to-be-optimized power system; constructing a data set by taking the input data of the alternating current power flow constraint as a sample and taking the output data of the corresponding alternating current power flow constraint as a label; adopting the data set to train a neural network in an off-line manner to obtain a trained neural network representing the alternating current power flow constraint; linearizing a neural network representing the alternating current power flow constraint, and embedding the linearized neural network into the mixed integer nonlinear model to replace the alternating current power flow constraint in the mixed integer nonlinear model; and solving the embedded model on line by adopting an optimization algorithm to obtain an optimal solution of the security constraint unit commitment problem of the to-be-optimized power system considering the alternating current power flow. According to the method, the solving precision and solving efficiency of the AC power flow SCUC problem can be improved.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The invention provides a visual guidance method and system for a large rocket cabin milling robot, and the method comprises the steps: 1, constructing a visual measurement system which comprises a hybrid robot, an area array structured light camera, a visual target, a target ball base, a product installation platform and a laser tracker, completing the system calibration, enabling the visual target to be installed on the target ball base, and enabling the visual target to be installed on the target ball base; the target ball base is fixed on the product mounting platform, and the laser tracker is used for calibrating the position of the target ball base; 2, collecting an image of a visual target through the area array structured light camera, analyzing the coordinates of the target, and resolving the relative pose of the robot relative to a product mounting platform, so as to determine the position of a theoretical cutting point according to a product design model; and 3, the area array structured light camera is used for collecting the three-dimensional shape of the surface of the to-be-machined product, the normal direction of the to-be-machined surface is calculated, and the direction of the tool is corrected according to the normal direction.

The application discloses an aperture measurement method and device based on distortion compensation and self-adaptive mean blur, and comprises the following steps: S1, performing binaryzation processing on collected images, correcting the distortion, then performing smoothing processing on the images and extracting a target region; S2, performing edge detection on an aperture target in the target region to detect pixels of an aperture edge; S3, detecting the aperture edge pixels to determine contour pixels of the aperture edge, then comparing the contour pixels with a threshold value to remove useless pixels and identify and acquire aperture contour features; and S4, based on the aperture contour features, using a least square method to fit a circle to calculate aperture size parameters. The application can realize non-contact and high-precision measurement of the aperture.

The invention discloses an optimal control method and system for reactive power compensation of a power distribution network, and the method comprises the steps: building a multi-target reactive power optimization model of the power distribution network based on the system parameters of the power distribution network, and carrying out the optimal control of the reactive power compensation of the power distribution network under the constraint condition of the multi-target reactive power optimization model of the power distribution network, solving the target function of the multi-target power distribution network reactive power optimization model by adopting a multi-target whale optimization algorithm to obtain an iterative optimization solution set output by the multi-target power distribution network reactive power optimization model, and processing the multi-target power distribution network reactive power optimization model based on the iterative optimization solution set and an improved multi-target grey wolf optimization algorithm to obtain a multi-target power distribution network reactive power optimization model. And taking a result output by the multi-target power distribution network reactive power optimization model as a node configuration optimal solution set of the power distribution network, and controlling the power distribution network by using the node configuration optimal solution set. According to the optimal control method and system for the reactive compensation of the power distribution network, the optimal control effect of the reactive compensation of the power distribution network is improved.

The invention discloses an airborne bistatic radar platform error compensation and target positioning method and system. The method comprises the following steps: constructing a measurement model for a bistatic radar system; the measurement model comprises a platform position motion model, a target position motion model, an observation equation of a platform to a target and a navigation error model of the platform; based on the measurement model, performing linear fitting on each platform position measurement and target position measurement by using a linear fitting model, estimating navigation error parameters of each platform according to fitting results, and correcting platform motion parameters according to estimated values of the navigation error parameters; and based on the corrected platform motion parameters, constructing a linearized target positioning model in combination with multi-platform observation data, and estimating the motion parameters of the target by using a weighted least square method to realize target positioning. Under the condition that no navigation source exists, the positioning precision of the airborne platform is improved, and then the positioning precision of the bistatic airborne radar on the non-cooperative target is improved.

This invention relates to the field of data processing technology and discloses a method, apparatus, and equipment for monitoring surface deformation based on parameter search interval optimization. The method, based on the location information of multiple buildings in a geographic coordinate system and multiple SAR images, determines at least one building-related region and at least one non-building region within a target area. It then determines a first residual elevation search interval for the building-related region based on the roof elevation value of at least one target building within the building-related region; determines a second residual elevation search interval for the non-building region; and performs temporal InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, a preset deformation rate search interval, and multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area. This significantly improves data processing efficiency while ensuring the accuracy of the temporal InSAR phase calculation and obtaining accurate surface deformation rate and elevation fields.

This disclosure relates to the field of remote sensing and mapping technology, and provides an adaptive distributed scatterer interferometry method, apparatus, and medium for mountainous areas. The method includes: acquiring radar image data and digital elevation model (DEM) data of the target monitoring mountainous area; calculating the radar vegetation index of each pixel in the dual-polarization synthetic aperture radar (DAP) image sequence and classifying it into multiple vegetation coverage levels to obtain a vegetation coverage grading atlas; identifying statistically homogeneous pixels in each image scene under the spatial constraints of vegetation coverage levels according to the grading atlas to determine a candidate set of distributed scatterers; further, using the grading atlas, performing adaptive coherence threshold filtering on each candidate point to obtain multiple distributed scatterers; and determining the long-term surface deformation results of the mountainous area based on the multiple distributed scatterers, the DAP image sequence, and the DEM data. This embodiment effectively improves the accuracy and reliability of distributed scatterer extraction and is suitable for deformation monitoring in complex mountainous areas.

The present invention discloses a method for solving the nonlinear dynamic response of a towing system, including the following steps: initializing the time and the number of calculation steps, with the initialization value being 0; initializing the speed and displacement values, with the initialization value being a given initial value; using the speed and displacement value of time step i as the speed and displacement value of time step i+1 to initialize the next time step; constructing a towing system unit matrix based on the node coordinate finite element method; constructing a conversion matrix from local coordinates to global coordinates based on Euler angle transformation, and determining the towing system unit matrix in the global coordinate system; determining the external force matrix by considering external forces, and constructing the towing system dynamic control equation based on the towing system unit matrix in the global coordinate system; solving the dynamic control equation response by using a symplectic difference algorithm, and judging whether the solution meets the conditions. The present invention effectively reduces the cumulative error caused by long-term calculation and analysis, and improves the solution accuracy.

The application discloses a VCSEL cold cavity three-dimensional vector mode solving method based on a transmission matrix-finite element hybrid. The method firstly constructs a complete epitaxial structure transmission matrix model containing an oxidation limiting layer to search for a resonance wavelength, and calculates DBR surface admittance at the resonance wavelength; then only the core active region is subjected to three-dimensional structured grid division and Nédélec edge element vector finite element discretization, and the DBR surface admittance is embedded into the weak form of Maxwell equation as an impedance boundary condition; a generalized non-hermitian eigenvalue problem is solved to obtain a cold cavity mode; finally, LP mode automatic classification is realized through a three-level algorithm of aperture restriction factor screening, azimuth angle Fourier decomposition and radial node counting. The application equivalently processes the DBR by the transmission matrix method, reduces the FEM calculation domain by one to two orders of magnitude, greatly improves the calculation efficiency while maintaining the three-dimensional vector precision, and correctly describes the influence of the longitudinal position of the oxide on the resonance condition.

The invention relates to the technical field of power system safety, and provides a power distribution system safety operation optimization method and system based on low inertia. The invention discloses a power distribution system safe operation optimization method based on low inertia. The method comprises the following steps: constructing an evaluation model based on a minimum inertia demand by taking minimization of the total operation cost of a system as an optimization target; under a set constraint condition, solving the evaluation model based on the minimum inertia demand by using a grey wolf algorithm to obtain a minimum inertia demand value at each moment; in the process of searching the minimum inertia demand value at each moment, the grey wolf algorithm adopts a convergence factor to determine a search area; the convergence factor is represented as the fractional order power of the ratio of the difference value of the maximum number of iterations and the current number of iterations to the maximum number of iterations. Response reliability of the system to sudden disturbance can be improved.

The invention discloses an operation state regulation and optimization method for a power distribution network containing a distributed power supply and a flexible load, and the method comprises the following steps: obtaining a topological structure and operation parameters of the power distribution network, and constructing a power distribution network operation state regulation model according to the topological structure and the operation parameters; on the basis of a power distribution network operation state regulation and control model, a traditional knowledge acquisition sharing algorithm is improved, and a spiral updating strategy is introduced to guide a search individual to approach a current candidate optimal solution along a spiral trajectory; and performing iterative solution on the operation state regulation and control model by using an improved knowledge acquisition sharing algorithm to obtain operation optimization configuration of the power distribution network. According to the method, the global search capability and the local convergence precision of the algorithm are remarkably improved, so that the operation optimization configuration can be quickly and accurately obtained, and the stability, the adaptability and the overall operation performance of the power distribution network when the power distribution network deals with renewable energy output fluctuation and load change are enhanced.

The present application relates to a kind of nuclear power process twin system simulation model parameter optimization method based on PSO-TD3 hybrid framework, comprising: based on demand, construct nuclear power process twin system simulation model, determine the optimization parameter contained in nuclear power process twin system simulation model;Loss function for measuring the combination performance of current each parameter of nuclear power process twin system simulation model is constructed, with the value of loss function minimization as optimization goal;With particle, each parameter of nuclear power process twin system simulation model is represented, based on particle swarm optimization algorithm, the PSO update function for updating the speed and position of particle is constructed;With the state parameter of PSO update function, TD3 state vector is constructed, and the output action of TD3 is mapped as the weight parameter of PSO update function;Based on optimal particle group, the parameter of nuclear power process twin system simulation model is updated and optimized.For solving the problem that nuclear power simulation model parameter space dimension is high, nonlinear is strong, traditional static optimization algorithm is easy to fall into local optimum.

The application belongs to the technical field of workshop scheduling, and particularly discloses a kind of inconsistent batch quantity flow flexible job shop scheduling method and system, it includes: for inconsistent batch quantity flow flexible job shop scheduling problem, with sub-batch quantity as one of decision variables to construct mixed integer linear programming model;Intelligent optimization algorithm is used to solve mixed integer linear programming model iteratively, in the iteration process, in the intermediate solution generated in each iteration, select part of elite solution, fix the machine allocation of each sub-batch in elite solution and the value of processing order variable of each sub-batch on machine, with the sub-batch quantity in elite solution as optimization variable, construct batch sub-problem model;Sub-problem model is solved, so that elite solution is optimized, and the intermediate solution after processing continues to the next round of iteration.The application can efficiently solve inconsistent batch quantity flow flexible job shop scheduling problem, and realize the improvement of processing efficiency and equipment utilization.