259results about How to "Reduce the difficulty of solving" patented technology

The present invention provides a mixed integer cone programming based intelligent distribution system synthetic voltage reactive power optimization method. The method comprises: inputting a distribution system structure and a parameter of a distribution system; establishing a time sequence optimization model of a distribution system synthetic voltage reactive power control problem considering various types of adjustment means; converting the established model into a mixed integer second-order cone model; solving the obtained mixed integer second-order cone model by using a mathematical solver capable of solving mixed integer second-order cone programming; and outputting a solution result. The method provided by the present invention greatly reduces solving difficulty and facilitates performing solution by using a solving tool, so that a complex mixed integer non-linear programming problem can be solved, cumbersome iteration and a large number of tests are avoided, and the calculation speed is greatly improved.

The invention discloses an optimization method for solving an omni-channel logistics distribution problem. In a first stage, a Lagrange relaxation technology is used to solve a LAP (Location-allocation Problem) problem. In a second stage, adaptive large-scale neighborhood search is used for solving a multi-vehicle-type vehicle path problem, a feasible solution can be searched in a large range in a solution space through one group of simple destruction and reconstruction algorithms, and a situation of falling into local optimum can be effectively avoided. Meanwhile, a simulated annealing acceptance criteria realized in the adaptive large-scale neighborhood search can guarantee the quality of a solution and the convergence of the algorithm, a result is output after the algorithm is executed for an appointed iteration number, a time constraint requirement can be met, and a good vehicle distribution scheme is solved for enterprises in limiting time. The simpleness of the Lagrange relaxation technology is combined with the efficiency of the adaptive large-scale neighborhood search, so that the integral solving efficiency of a mixed algorithm is high, and the problem of omni-channel logistics distribution can be effectively solved.

Disclosed are an active distribution network optimal configuration structure and a configuration method of the active distribution network optimal configuration structure. The active distribution network optimal configuration structure is formed by connecting an active distribution network optimizing system, a fuel oil generator set, a wind power generation set, a photovoltaic power generation set, a storage battery energy storage device, an EV charging pile, an AMI and a load into a distribution network through respective converters, and the active distribution network optimizing system, the fuel oil generator set, the wind power generation set, the photovoltaic power generation set, the storage battery energy storage device, the EV charging pile, the AMI and the load are connected into a large power grid through a public connecting point. Isolated island operation and grid-connected operation of the active distribution network can be achieved by controlling the active distribution network through an active distribution network optimizing system. A dual-layer optimization plane design model suitable for the active distribution network is provided, and comprehensive optimization of distributed power supplies of the active distribution network and scheduling optimization of the distributed power supplies can be achieved.

The invention discloses a method for calibrating in-orbit exterior orientation parameters of push-broom optical cameras of remote sensing satellite linear arrays. The method includes steps of (1), acquiring information of high-precision control points; (2), acquiring interior orientation elements of the cameras at the control points, acquiring auxiliary data of orbits and the like and building rigorous imaging geometric models of various control points; (3), computing positioning errors of the various control points, eliminating coarse and poor control points and acquiring information of preliminarily calibrated control points; (4), building geometric calibration mathematical models of exterior orientation elements of the cameras, and computing theoretical pointing vectors and actual pointing vectors of the various control points; (5), creating three-axis rotation error equations among the theoretical pointing vectors and the actual pointing vectors and linearly processing the equations; (6), solving parameters of the error equations by a least square process to acquire calibration exterior parameters. The method has the advantage that after errors of a system are compensated by the aid of the calibration exterior parameters, the cameras are stable in non-control positioning precision.

Provided is a power distribution network intelligent soft switch operation optimization method with simultaneous consideration of switch motion. According to a power distribution system, connection relation of circuit parameters, load level and network topology, system operation voltage level and branch current restriction, distributed power supply connection position, type, capacity and parameters, intelligent soft switch connection position, capacity and parameters, load and distributed power supply operation characteristic prediction curves in an operation optimization cycle, a system reference voltage and a reference power initial value are input; a sequential optimization model of cooperation operation of a power distribution network connection switch and an intelligent soft switch is established; conic model conversion of an object function and non-linear constraints in the sequential optimization model of cooperation operation of the power distribution network connection switch and the intelligent soft switch is carried out according to a conic optimization standard form; linearization of the object function is carried out, the non-linear constraints are converted into linear constraints, second order cone constraints or rotary cone constraints, and solving of the obtained mathematic model is carried out through a CPLEX solver. The provided method avoids tedious iteration and a lot of tests.

The invention provides a dynamics performance parameter optimizing method of a high-speed train, relates to the field of parameter design optimizing based on the dynamics simulation analysis of the high-speed train, and aims at effectively replacing a dynamics simulation model of the high-speed train by a comprehensive target neural network agent model and combining the design analysis and the multi-target optimization algorithm of the high-speed train in the multi-disciplinary field to analyze and optimize the dynamics simulation approximation model of the high-speed train. The method specifically comprises the steps of building a multi-rigidity dynamics simulation model for the high-speed train; determining related important input/ output design spaces; selecting sampling strategy to obtain a design space sample set suitable for the dynamics performance analysis of the high-speed train; improving the generalization accuracy of the comprehensive target neural network by the bayesian regularization method; adjusting the number of nodes in a hidden layer to build the comprehensive target neural network agent network model of which the error is controlled to be within a certain of range; performing multi-target optimization through the intelligent differential evolution algorithm by using the improved comprehensive target neural network agent network model to obtain the optimized high-speed train design parameters. The method is mainly applied to the dynamics analysis and design optimization of the high-speed train.

The invention discloses an active distribution network multi-stage planning method. How to comprehensively consider the uncertainty of source-load network storage in distribution network planning is aproblem that needs to be studied in-depth. The invention comprises the following steps: inputting power system parameters, historical operation data, regional development planning demand and predicted uncertain factor data; Calculating the shadow price of each planning decision quantity in each running scenario with multi-time scales; According to the shadow price and the occurrence probability of each scene, establishing the running constrained scene library including extreme scenes and classical scenes. According to the constrained scenario library, modifying the upper-level multi-stage planning optimization model. The invention screens out the planning scene database with pertinence and typicality to revise the optimization model of planning investment, which is used to simulate the feedback of the operation decision-maker to the planning decision-maker, thereby improving the observability of the massive operation scene result of the distribution network, hierarchically solving thedecision variables of different time scales, and reducing the solution difficulty.

The invention relates to a mechanical arm tail-end pose error correction method based on elliptic characteristics. The method comprises the steps of obtaining an image of a round-mouth target tank, extracting a target ellipse from the image, determining the relative position and axial relation between a tank mouth of the round-mouth target tank and an actuator according to the elliptic characteristics of the target ellipse, determining a current pose of the actuator, joint angles of all joints of a mechanical arm and the pose deviation value of the actuator according to the relative positions,the axial relation and configuration constraint of the mechanical arm, and adjusting the mechanical arm according to the pose deviation value till the pose deviation value of the actuator is smallerthan a preset value. By means of the method, the problems that single elliptic characteristic positioning has ambiguity and axial rotation poses cannot be determined are solved, accurate guidance canbe provided for the mechanical arm to collect samples and put the samples into the target tank, and control errors caused by local flexibility or joint clearance are corrected. The invention further provides a mechanical arm tail-end pose error correction system based on the elliptic characteristics.

The invention discloses a building method of a large-scale and high-reliable filing storage system. The technical problem to be solved is to provide a large-scale filing storage system and improve the reliability of the filing storage system on the basis of filing data. The technical scheme is as follows: adopting the multi-copy distributed storage and parallel recovery methods of files to store and recover the large-scale filing storage system, designing a continuous-time Markov model to describe the fault recovery process of the large-scale file storage system so as to obtain optimized parameters of the large-scale filing storage system according to the Markov model, and finally building the storage system according to the obtained optimized parameters. The model of the invention is simple, lowers the difficulty of building the high-reliable filing storage system and improves the reliability of the filing storage system, and the built storage system has extensible structure and high cost performance.

The invention discloses an urban rail line passenger flow peak prediction method based on linear programming, and the method comprises the following steps: 1), representing a starting station and an ending station of one travel through OD, and enabling an urban mass transit train working diagram and passenger flow distribution to be projected to a single passenger flow-operation graph network through the OD parameters of passenger flow, thereby converting a passenger transportation peak value of an urban mass transit network into a multi-source multi-commodity network max-flow solution; 2), obtaining the number of passengers in one rail transportation of one train based on the passenger flow-operation graph network obtained at step 1), and obtaining the peak value of the whole urban mass transit line through summation. The method provided by the invention simplifies the travel process of urban mass transit passengers into the passenger flow-operation graph network through network conversion, and improves the calculation efficiency. Meanwhile, the method avoids the prediction of the behaviors of passengers, employs the time of trains expected by the passengers to achieve the division of a travel time window, and improves the objectivity.

The invention relates to the field of active power distribution network voltage control and particularly discloses an active power distribution network voltage control method and system based on a distributed power supply. The method includes that an upper layer realizes the active power distribution network global voltage control by taking long-time scale economic operation of the active power distribution network as the principle by means of a power distribution network dynamic reconstruction method; the lower layer realizes the active power distribution network local voltage control by taking a feedback automatic voltage regulator and a capacitor as the controlled objects and taking the branch voltage level as the optimization object; and the voltage active control double-layer models coordinate with each other to make a more comprehensive voltage optimization strategy so that the voltage active control of the active power distribution network can be realized. Meanwhile, based on the voltage active control double-layer models, the trend constraint of the upper layer global voltage regulating model is relaxed by means of a second-order cone, the voltage active control double-layer models can be equivalently converted to a single-layer decision model by means of the kuhn-tucker condition, therefore, the model solution complexity is simplified on the basis of guaranteeing the active power distribution network voltage regulating performance, and the algorithm efficiency is improved.

The invention discloses a power distribution network double-layer optimization scheduling method and device, and the method comprises the following steps: obtaining the basic information of a load, wind power, photovoltaic and energy storage equipment in a load aggregator LA, and obtaining the basic information of a topological structure, a power supply and a load of a power distribution network;inputting the basic information of the load, wind power, photovoltaic and energy storage equipment in the load aggregator LA and the basic information of the topological structure, the power supply and the load of the power distribution network into a pre-established power distribution network double-layer optimization scheduling model to obtain an optimization scheduling result; wherein the powerdistribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model; by establishingthe power distribution network double-layer optimization scheduling model considering participation of the LA, the LA obtains economic benefits and plays a positive role in safe and economic operationof the power distribution network at the same time.

The invention relates to a thermal model modeling method for a lithium ion battery pack based on an air-cooling heat dissipating mode, and belongs to the technical field of batteries. According to the purpose, the thermal model modeling method for the lithium ion battery pack based on the air-cooling heat dissipating mode considers battery internal resistance and heat exchange coefficient time-varying condition, ensures model precision and reduces model solution difficulty. According to an energy conservation equation of the lithium ion battery pack, battery internal resistance data within the temperature range of 25-45 DEG C is fitted to obtain a linear function relational expression between the battery internal resistance and battery temperature, and finally a heat model of the monomer lithium ion battery is finally obtained. The defects that analytical solution cannot be obtained from an existing battery pack heat model, model accuracy is low, and the number of variables to be solved is large are overcome. The battery internal resistance and the heat exchange coefficient time-varying condition are considered, the analytical solution of the battery pack temperature can be obtained, model accuracy is ensured, the model solution difficulty is reduced, the number of variables to be solved is reduced, and a theoretical basis is provided for online estimating temperature of the lithium ion battery pack.

The invention discloses a method for computing the maximum access capacity of distributed power sources on the basis of expansion second-order cone programming. The method includes inputting related information of power distribution networks and setting the computational accuracy and the maximum number of iteration of the maximum deviation of cone relaxation; judging whether the numbers of iteration exceed the maximum number of iteration or not; taking the maximum access capacity of the distributed power sources in systems into account according to the related information of the power distribution networks and building models for computing the maximum access capacity of the distributed power sources; converting the models for computing the maximum access capacity of the distributed power sources into second-order cone programming models according to standard forms of second-order cone programming; carrying out computation and solution by the aid of mathematical solvers for solving second-order cone programming, outputting solution results and judging whether the maximum deviation of cone relaxation meets given accuracy requirements or not; adding cutting plane constraints on the basis of the second-order cone programming models and integrally forming expansion second-order cone programming models. The method has the advantages that complicated nonlinear programming problems can be solved, tedious iteration and large quantities of tests can be omitted, and the optimal access schemes for the distributed power sources can be quickly obtained.

The invention discloses a residual carrying capacity evaluation method applicable to the condition of thrust drop failure. The method includes: judging whether a carrier rocket can be sent into the intended target orbit by using available thrust and residual fuel after the thrust drop failure of the carrier rocket occurs; if so, sending the carrier rocket into the intended target orbit by using the available thrust and residual fuel, if not, determining whether the carrier rocket can enter a safe orbit with the available thrust and residual fuel, and if the available thrust and residual fuel meet the condition for entering the safe orbit, sending the carrier rocket into a rescue orbit between the intended target orbit and the safe orbit or into the safe orbit with the available thrust andresidual fuel; otherwise, determining that the remaining carrying capacity is insufficient to enter the safe orbit; quantifying the residual carrying capacity as solving the minimum normal working time required for an engine to enter the intended target orbit and the safe orbit, which reduces the solving space of the rescue orbit and improves the solving efficiency.

The invention relates to a convex-difference-planning-based calculation method of the maximum access capability of a distributed power supply in a distribution network. The method comprises: distribution network structure and parameter information is inputted, and a calculation accuracy value, an initial penalty coefficient and an iteration number of times are set; according to the distribution network structure and parameter information, a distributed power supply maximum access capability calculation model is established by considering the access capability maximization of the distributed power supply in the system; for nonlinear constraints in the maximum access capacity calculation model, conversion into a second-order cone programming model is carried out by linearization and second-order cone relaxation, and the model is solved to obtain an initial solution to the distributed power supply maximum access capability calculation model; a convex difference inequation is introduced into the second-order cone programming model and linearization is carried out, an objective function is updated; a penalty item is added into the objective function, and conversion to a convex difference programming model is carried out; the convex difference programming model is solved and whether a convergence condition is satisfied is determined; an iterative penalty factor is updated and solution calculation is carried out again; and a result is outputted. According to the invention, the calculation speed is increased substantially; and the maximum access plan of the distributed power supplyis obtained quickly.

An active power distribution network asymmetric operation optimization method based on an intelligent soft open point includes the following steps: inputting a structure and parameters of a power distribution system; establishing an active power distribution network asymmetric operation optimization model based on an intelligent soft open point on the basis of the structure and parameters of the power distribution system and also on the basis of consideration of system loss, system voltage imbalance and current imbalance at an outlet side of a power distribution transformer; conducting linearization and convex relaxation on non-linear constrained conditions in the active power distribution network asymmetric operation optimization model based on the intelligent soft open point according to a semidefinite programming standard form, and converting the model into a semidefinite programming model; solving the obtained semidefinite programming model through a mathematic resolver solving semidefinite programming; and outputting a solving result, and verifying accuracy of semidefinite relaxtion. Active power distribution network asymmetric operation optimization problems based on the intelligent soft open point can be described in a unified manner. Model solving difficulties are reduced, the calculating speed is greatly improved, and an optimal system operation scheme can be obtained quickly.

The embodiment of the present application discloses a fisheye lens image distortion correction method and device. The method includes: acquiring an orthogonal oblique grid target image; extracting a grid center line according to the orthogonal oblique grid target image; calculating a distortion center according to the grid center line; Calculate the distortion coefficients k1 to k6 according to the distortion center; and correct the image captured by the fisheye lens by using a six-parameter correction model according to the distortion center and the distortion coefficient. The fisheye lens image distortion correction method provided by the embodiment of the present application only needs to use a single target, the calculation process is simple, the calculation amount is small, and the accuracy is high, and the fisheye lens image distortion can be effectively eliminated, and the center of the lens and the center of the image are not aligned. Image asymmetry error caused by coincidence.

The invention discloses a rectangular intelligent layout method and system based on knowledge migration, and belongs to the field of optimized blanking of structural parts, and the method comprises the steps: (1) selecting a plurality of groups of parts to be subjected to layout as source tasks, and extracting the relevant information of the parts and plates; (2) solving the selected multiple groups of source tasks by using an ant colony reinforcement learning algorithm, and storing the learned optimal knowledge matrix into a knowledge base; (3) extracting two groups of source tasks most similar to the target task to be solved from the knowledge base, and linearly migrating the knowledge matrixes corresponding to the two groups of source tasks to the target task; (4) enabling the target task to calculate an optimal layout scheme by means of a migration knowledge matrix and an ant colony reinforcement learning algorithm; and (5) outputting an optimal layout scheme of the target task. According to the rectangular intelligent layout method based on knowledge migration, the plate utilization rate can be increased, the solving speed can be increased, and the rectangular intelligent layout method has good practicability in solving the large-scale and medium-scale rectangular layout problem.

The invention discloses a self-adaptive robust day-ahead optimization scheduling method for an optical storage charging tower, and the method comprises the steps: firstly, building a day-ahead energy-standby collaborative optimization scheduling model by taking the minimum daily operation total cost of the optical storage charging tower as a target; considering Influence of photovoltaic output andload uncertainty on day-ahead scheduling, and taking day-ahead energy-standby collaborative optimization scheduling and the real-time energy balance adjustment as a first-stage decision and a second-stage decision, and establishing an adaptive robust three-layer optimization scheduling model; Secondly, describing the uncertainty set by adopting a representative scene, introducing an auxiliary variable to replace the operation cost of the worst scene in the second stage, and realizing decoupling of inner layer min-max problem, so that three-layer optimization is converted into a single-layer robust optimization model containing a limited number of scenes; And finally, testing and solving are carried out in an actual optical storage charging tower system. According to the method, the systemuncertainty can be effectively processed, the complexity of a three-layer optimization model is reduced, meanwhile, a charging tower day-ahead scheduling strategy is obtained, and the obtained resulthas robustness.

The invention belongs to the field of milling processing, and discloses a cutter shaft vector optimization method based on covariant field functionals. The method comprises the following steps of (a), determining a processing requirement which should be satisfied by to-be-optimized cutter shaft vectors in actual processing, and converting a machining requirement to an objective covariant field functional and a restrained covariant field functional; (b), performing weighting summation on the covariant field functional, thereby obtaining an optimization model for the to-be-optimized cutter shaft vectors; and (c), discreting the model and performing solving for obtaining the optimized cutter shaft vector. The cutter shaft vector optimization method has advantages of ensuring smoothness of the cutter shaft vector and a processing track, preventing excessive cutting or collision of a cutter and a workpiece, and realizing suitability for workpiece curved surfaces with different parameters. Furthermore the cutter shaft vector optimization method has advantages of high processing precision and high surface quality of the processed workpiece.

The invention relates to a distributed wind power generation grid-connected posterior probability line loss allocation method considering wind speed intensity. The method comprises the following steps: firstly determining a research object, namely a specific distributed wind power generator set, to definitely research the wind speed characteristics of an area where the research object is located; then establishing the practicability discrete output force characteristics of the distributed wind power generator seat based on scene decoupling-curve fitting and the wind speed intensity; then carrying out load flow calculation containing the distributed wind power generator set and line loss allocation calculation based on a load flow track algorithm; solving the line loss rate allocated by the distributed wind power generator set under different scenes again; and finally determining the line loss allocation rate of the distributed wind power generator set according to each occurrence probability of wind speed intensity of the area. According to the method, the probability distribution characteristics of territorial wind speed intensity are considered firstly to carry out probability line loss allocation research; and the allocation method can further help power supply departments to price wheeling cost allocation for the grid-connected distributed wind power generator set on the basis of determining the line loss allocation rate.

The invention relates to the field of new energy grid connection. The invention aims to provide a heterogeneous energy complementary power generation scheduling method based on grid-source mutual feedback. The method considers both the power supply side and the power grid side, and construct a heterogeneous energy complementary power generation short-term optimization scheduling model based on grid-source mutual feedback; and two targets with the maximum power-load matching degree and the minimum clean electric energy abandoning quantity are decomposed into sub-models, thereby reducing the solving difficulty, and improving the calculation efficiency; and new energy is consumed as much as possible under the condition that the power supply and the load are highly matched, and the cascade water and electricity abandoning amount is reduced, thereby maximizing the utilization of clean energy, and providing stable high-quality electric quantity for a power grid.