131 results about "Quantum genetic algorithm" patented technology

The invention discloses an optimized planning method of an electric vehicle charging station based on whole life cycle cost, wherein the optimized planning method analyzes the cost benefit of a charging station and calculates the whole life cycle cost. The invention suggests estimation of a charging station capacity by means of road network traffic flow information. Maximization of net present value profit is used as an optimization target. The position and the capacity of the charging station are determined according to the road network traffic flow, electric network power quality and economic performance and user charging requirement as constraint conditions. Furthermore, the invention provides a charging station optimization planning model based on the whole life cycle cost, and furthermore the charging station optimization planning model is solved according to a quantum genetic algorithm. The optimized planning method can plan the electric vehicle charging station effectively and accurately, thereby obtaining an optimal station address and an optimal capacity, and calculating a maximal net present value. The optimized planning method compensates insufficiencies in present other electric vehicle charging station planning methods to a certain extent, and furthermore can obtain higher convergence speed and more accurate value in actual operation.

The invention provides a route selection method for a modeling quantum genetic algorithm in a wireless network. In the method, a hierarchical node management model is established through interaction among convergence nodes, cluster head nodes, inter-cluster nodes and end nodes, and the energy state management of each target node is realized. The route selection method for a node comprises the following steps of: firstly, reading the state information, and optimizing the initial popularization in the quantum genetic algorithm; then, calculating the optimal route between the source node and the target node by using the full coherency, the dynamic quantum revolving door and other strategies through the characteristics such as efficient searchability, parallel quantum calculation and the like of the quantum genetic algorithm. The overall energy consumption of the network is kept to be minimized to the furthest extent, and the life of the wireless sensor network is prolonged.

The present invention relates to an electric taxi charging station planning method based on an adaptive quantum genetic algorithm. The method comprises the steps of (1) initializing basic data needed by planning, and estimating the value range of a charging station number, (2) randomly generating a chromosome corresponding formula, and forming a population, (3) carrying out solution space transformation and mapping a randomly generated angular space to a charging station address coordinate space, (4) using a Voronoi diagram to divide the service range of a charging station corresponding to chromosome, (5) calculating the average waiting time in queue of each charging station based on the charging requirement in the service range, (6) planning the expression of total society whole year total cost target function minimization according to electric taxi charging stations, (7) carrying out a quantum rotation gate updating operation and a quantum bit variation operation, (8) returning to the step (3) to carry out loop calculation until a convergence condition is satisfied. The method has the advantages that the optimized layout of charging station site is realized, the influence on the planning by taxi distribution, passenger demand distribution, and a road network structure can be reflected, and the method is effective and practical.

The invention discloses a method and a device for detecting cloth flaws based on adaptive orthogonal wavelet transform. Manual eye detection and an original automatic flaw detecting method in which wavelet transform is carried out after a wavelet basis is manually selected are replaced. The defects of traditional manual eye detection, such as low detection speed, low efficiency, false detection and high detection leaking rate are overcome. The problem of the original flaw detecting method based on wavelet transform that the detection precision is low as the wavelet basis is not optimized is solved. The optimal wavelet basis matched with a cloth texture is selected by an improved quantum rotating gate quantum genetic algorithm, the quantum rotating angle is regulated by a dynamic strategy, fine adaptive search is realized, the variety is enriched through introducing mutation operation, and the optimization capability of the algorithnm is improved through combining chaotic search. The flaw detecting method has the advantages of high speed, high accuracy, simplicity in operation and high efficiency and has great application prospects.

The invention relates to a micro-grid economic and optimal operation and scheduling method based on an improved quantum genetic algorithm. The micro-grid is in a grid-connected mode operation state and comprises multiple micro sources and loads, wherein the loads comprise electric loads and thermal loads; and the micro sources comprise a micro turbine, a wind turbine, a photovoltaic cell, a fuel cell, a storage battery and an electric vehicle. The method comprises the following steps: S1, state information of each load and each micro source in the micro-grid is acquired; S2, with minimum of operation cost and pollutant treatment cost as a target, a multi-target economic scheduling model is built; S3, the improved quantum genetic algorithm is adopted for carrying out optimal solution on the multi-target economic scheduling model, and the optimal active power of each micro source is acquired; and S4, according to the optimal active power of each micro source, active power output by each micro source is controlled. Compared with the prior art, the micro-grid formed by distributed power sources operates in a more economic, flexible and environment-friendly mode, and power generation advantages of the distributed power sources can be taken.

The invention discloses a water resource optimization scheduling method based on an improved multi-objective quantum genetic algorithm. The steps are as follows: firstly, obtain the basic information data of the water resource system; secondly, establish a water resource optimal scheduling model; and then execute the improved multi-objective quantum genetic algorithm. The algorithm solves the optimal pareto non-inferior solution set of the water resource system, and uses certain rules to select the final result from the optimal solution set. The invention realizes global optimization, improves calculation efficiency, and satisfies the requirement of selecting a multi-objective optimal scheduling scheme for a water resource system.

A bearing fault diagnosis method based on a quantum genetic algorithm optimized support vector machine of the invention comprises the following steps: (1) acquiring a vibration signal of a bearing; (2) calculating dimensionless indexes; (3) optimizing model parameters C and Sigma of a support vector machine based on a cloud model quantum genetic algorithm; (4) training a support vector machine model; (5) performing fault diagnosis using the support vector machine model; and (6) outputting a bearing fault diagnosis result. The bearing fault diagnosis method of the invention has the advantage of high diagnosis accuracy. A new method is provided for solving the problem of bearing fault diagnosis.

The invention discloses a vehicle inertia suspension structure and a parameter determination method thereof, and belongs to the technical field of vehicle suspension vibration isolation. The vehicle inertia suspension is formed by serially connecting two stages; the first stage consists of two elements of a spring and a damper connected in parallel; and the second stage consists of two elements of an inertia container and a spring connected in parallel. The two elements of the inertia container and the spring connected in parallel are used for generating the acting effect of parallel resonance under specific conditions to realize the inhibition of vehicle body mass vibration so as to effectively buffer and attenuate the road surface unevenness impact. A quantum genetic algorithm is used for optimally calculating suspension parameters; the simulated analysis indicates that the vehicle inertia suspension structure and the parameter determination method thereof, disclosed by the invention, obviously improve the suspension performance indexes compared with a transmission passive suspension; and the vehicle driving smoothness and safety are both effectively improved.

The invention discloses an improved quantum genetic algorithm-based micro-grid energy storage locating and sizing optimization method. The method comprises the following steps: establishing an energy storage locating and sizing optimization model, wherein the energy storage locating and sizing optimization model comprises a target function formula and a constraint formula; improving a quantum genetic algorithm; and solving the energy storage locating and sizing optimization model by using the improved quantum genetic algorithm. According to the method, the energy storage locating and sizing optimization model is established, energy storage whole life cycle period cost, peak clipping-valley filling earning and grid loss earning are taken as targets, and the trend, energy storage charge-discharge and energy storage charge-discharge energy balance are constrained and considered; the quantum genetic algorithm is corrected, the dynamic adjustment strategy of a quantum revolving door revolving angle is used for improving the search efficiency, and a selection operation implemented by a simulated annealing method and a good point set cross operation can avoid local optimum; a 34-node micro grid is adopted to carry out verification so as to indicate that the disclosed algorithm is feasible, and the convergence efficiency of the quantum genetic algorithm and the ability of jumping out of local optimum are effectively improved.

The invention discloses a fault feed line positioning method suitable for a power distribution network with high proportion of connected distributed power supplies. The method comprises the steps of constructing a power distribution network topology model graph which comprises high-proportion DG; constructing a switching function according to the constructed power distribution network model; constructing a fitness function according to the constructed power distribution network model; performing coding on the constructed model, namely performing quantum bit coding through using a wire inlet breaker, a section switch and a connecting switch of each feed line segment of the power distribution network as nodes; and when a true fault of a certain feed line occurs, performing the following fault determining steps according to an actual operation condition: reading the on-off state value of each breaker, the section switch and the distributed power supply connecting switch by an FTU system,and transmitting to a main station; after the main station receives fault information, performing simulation verification by means of a quantum genetic program; outputting a result, reading the faultfeed line and determining the fault switch. According to the method of the invention, fault positioning time is reduced through the improved quantum genetic algorithm, and real-time performance in fault positioning of the power distribution network is improved.

The invention discloses a three-dimensional packing overall optimization method and system for putting multiple goods and materials into multi-specification packets and belongs to a method for intelligentized processing of goods and material packing. A packing overall optimization calculation module is started, the packing overall optimization calculation of one task is finished by combining a quantum genetic algorithm with a heuristic three-dimensional packing algorithm, and packing schemes are output. Reasonable schemes to be presented are stored in a database after judgment. Through organic combination of the heuristic three-dimensional packing algorithm and the quantum genetic algorithm, the overall packing optimization calculation can be performed for the multiple goods and materials which are to be placed into multi-specification packet containers. Compared with existing optimization calculation for processing a single packet container or one type of packet containers, the three-dimensional packing overall optimization method and system has the obvious overall advantages.

The invention discloses a high-voltage switch cabinet insulator electric field optimization method based on a quantum genetic algorithm. The method comprises the following steps that 1) a high-voltage switch cabinet insulator geometric model is built; 2) the high-voltage switch cabinet insulator model is subjected to electrostatic field simulation to obtain the maximum electric field intensity value, and structural factors for influencing the electric field distribution and the maximum field intensity are determined through changing variable structural parameters; 3) a population is initialized; 4) an objective function is determined, and a fitness degree function is calculated, wherein the objective function of an individual is the electric field intensity corresponding to the parameters; 5) for the individual population consisting of binary gene codes, the variation is carried out after the selection and the full-interference crossing; and 6) whether the quantum genetic operation stop condition is met or not is judged, if the stop condition is not met, the operation returns to the first step, and if the stop condition is met, the corresponding response value is calculated according to the optimized structure parameters obtained in the fifth step, and the maximum electric field intensity value is obtained. The method can realize the optimization on the high-voltage switch cabinet insulator electric field.

The invention relates to independent component analysis global search method of realizing the high spectrum fine classification under no prior knowledge situation, the method including: reading in the high spectral data, the establishment of independent component analysis model based on the kurtosis, the center of the data, the ball of data, the iterative solution based on quantum genetic algorithm, independent component compositor, two value of the image, the feature classification. The invention method can established on the circumstance of no data background model, using the self high statistical data to achieve the feature fine unsupervised classification of the high spectral data; at the same time, avoided to plunge in the local best solution problem in the independent component analysis solution process, and compared with the traditional genetic algorithm, the invention used quantum genetic algorithm has less number of iterations, fast convergence, high search efficiency and the strong overall search capability and so on features.

The invention discloses a wavelet threshold image denoising method based on an F-type double-chain quantum genetic algorithm. First of all, single-value mapping processing is performed on a coding space, the search space of the algorithm is reduced, and search density is increased; secondly, a self-adaptive step length factor is introduced during quantum updating to enable a step length to change along with the gradient change of a target function at a search point so that the problem of global optimal solution search difficulty caused by an "oscillation" phenomenon generally existing in a conventional searching optimization algorithm at present is effectively solved; and finally, a pi / 6 gate is brought forward during chromosome variation updating so that the disadvantage is improved that conventional NOT gate variation cannot update quantum bit probability amplitude. According to the invention, an F_DCQGA optimization algorithm is also applied to a threshold selection mechanism of wavelet threshold de noising, at the same time, a self-adaptive threshold function is brought forward, and accordingly, a conventional wavelet threshold denoising method is improved. The method provided by the invention improves the convergence speed and the search precision of a wavelet threshold function.

The invention discloses an automatic adjusting device and method for an automobile driving seat based on fingerprint identification. The device is mainly composed of an ARM control unit, a fingerprint reading module, a position sensor and a seat automatic adjusting mechanism, wherein the fingerprint reading module is used for reading fingerprint information of a driver, identification comparison and distinguishing are conducted by an ARM according to the fingerprint information, the sear adjusting mechanism adjusts and sets the seat to different coordinates according to pulse width modulation signals given by the ARM, the position sensor detects seat position information in the adjustment process to achieve precise running, and therefore the effect is achieved that automatic preparation working of a vehicle is conducted before driving according to the driver's identification. In addition, coordinate data after the adjustment is collected, a comfort degree of the seat adjusted and used by the driver is calculated by a quantum genetic algorithm, compared with current seat adjustment, start-up and driving preparation, the automation degree and the integration degree are higher, the probability of human errors is reduced, and the humanization of driving operation is enhanced.

The invention relates to a quantum-genetic-algorithm-based fault diagnosis method for a medium-voltage distribution network. The method is characterized in that the method comprises the following steps that: step one, an improved medium-voltage distribution network fault diagnosis model is established by employing actual and expected values of element movement and combining the circuit breaker failure protection and the protection state of automatic opening and closing of the circuit breaker; and step two, a quantum-genetic-algorithm-based improved medium-voltage distribution network fault diagnosis model is solved to carryout fault diagnosis on the medium-voltage distribution network. According to the invention, the distribution network fault diagnosis model is established by analyzing the element type and the protection configuration situation of the distribution system; protection and the movement situation of and circuit breaker after a fault scene are simulated; and then the fault model is solved by using the quantum genetic algorithm. Therefore, functions that a fault element can be localized accurately after fault occurrence at a distribution network and the fault of the distribution network can be diagnosed rapidly and comprehensively can be realized.

A parameter induction underdamping steady-state matching stochastic resonance weak feature enhancement method comprises: employing the Hilbert transform to perform demodulation of obtained vibration signals, releasing fault feature frequency to a low-frequency area, and obtaining a corresponding envelope; inputting a parameter induction underdamping steady-state matching stochastic resonance system, taking a weighting signal-to-noise ratio of system resonant response as a target function of the quantum-inspired genetic algorithm, optimizing a system parameter, a damping ratio and a scale factor, triggering a particle movement mode and resonance cooperation between a transition rate and system input, and allowing the fault feature frequency to be located in a narrow transmission band of a stochastic resonance nonlinear band-pass filter; and setting a parameter induction underdamping steady-state matching stochastic resonance system according to an optimal parameter pair, inputting the envelope into a set stochastic resonance system, calculating and obtaining a resonance response of the system and performing analysis, and realizing enhancement and extraction of a mechanical fault feature frequency. The parameter induction underdamping steady-state matching stochastic resonance weak feature enhancement method improves weak feature enhancement and extraction capabilities of stochastic resonance.

The invention provides a multi-satellite task scheduling method and a multi-satellite task scheduling system. The method comprises the steps of acquiring the initial preset number of task sequences, and selecting the task sequence with the maximum adaptability degree from the initial preset number of task sequences as an initial target task sequence; carrying out an operation according to the initial target task sequence through a quantum genetic algorithm to obtain the new preset number of task sequences; and selecting a new target task sequence from the new preset number of task sequences, and carrying out an iterative operation according to the new target task sequence through the quantum genetic algorithm to obtain a scheduled task sequence. Therefore, the use efficiency of satellites is improved.

The invention provides a complicated well hole track optimization method based on a fast self-adaption quantum genetic algorithm. The method comprises the following steps that through the analysis of an Fibonacci number sequence, the condition that the number sequence has a negative index characteristic is discovered, and the characteristic is introduced into a quantum rotation gate rotating angle step length updating strategy, wherein the space complexity of the algorithm is not increased, the time complexity of the algorithm is lowered, the algorithm efficiency is greatly improved, and the operation time of the algorithm is shortened; secondly, any one quantum position is enabled to be in one-to-one correspondence to points on a Bloch ball surface, so that the ergodicity of a solution is improved; finally, by aiming at a multi-target complicated three-dimensional well hole track optimization problem, under constraint conditions of each well section, casing pipe length and target vertical well depth, the FAQGA optimization is used for practically measuring the well depth TMD; a well body, a well inclined angle, a well inclination azimuth angle and well section curvature parameters are optimized, thus realizing the precise and efficient well hole track optimization.

The invention provides a multi-correlation vector machine water quality prediction method and system based on quantum genetic algorithm optimization. The method comprises: establishing a correlation vector machine water quality prediction model according to a monitored historical data time sequence of a certain water quality index; establishing different association vector machine sub-models, wherein each established sub-model performs description of different angles on the object from different characteristics of each piece of data; learning and training the BP network by using the predictionresults obtained by the sub-models to obtain a nonlinear combination function model, and further completing effective fusion of the sub-models; embedding a quantum genetic optimization algorithm to optimize and select initial parameters of the BP network, so that the convergence rate of the network is increased to achieve global optimization, and finally water quality prediction of the multi-correlation vector machine based on quantum genetic algorithm optimization is achieved. The selection of the kernel function is not limited by any condition, and the credibility of the prediction result can be given when the prediction value is given.

The invention belongs to the technical field of bioinformatics and discloses an RNA secondary structure prediction method for a quantum genetic algorithm based on multi-population assistance. According to the method, a stem pool and a stem compatibility matrix of an RNA sequence is established according to the RNA sequence; quantum bit vectors are used to initialize multiple chromosome populations; quantum measurement is performed on each population; optimal individuals are acquired according to measurement results; the optimal individual b in all the populations is obtained and used to replace worst individuals, nonhomologous to b, among the optimal individuals in other populations, then all the populations are updated by use of different rotational angles, and other populations not participating in replacement are updated by use of a fixed rotational angle; and the process is iterated till a stop condition is met. Through the method, the global search capability and search efficiencyof the quantum genetic algorithm are effectively improved, and the evolution algebra of the genetic algorithm is lowered. Meanwhile, all the populations suppress competition and cooperate mutually, so that the globality of the algorithm is improved, and prediction accuracy is substantially enhanced.

A PMU optical configuration method for a ship electrical power system based on direct solvable power flow includes step a, obtaining power flow equations and node incidence matrixes under different working conditions; step b, describing features of the power flow equations by using system node incidence matrixes; step c, determining a power flow equation of the ship electrical power system under a certain working condition and whether a PMU configuration scheme satisfies direct solvable power flow; step d, determining whether the PMU configuration scheme satisfies direct solvable power flow under different working conditions on the basis of the step c; step e, defining N-1 voltage phasor solvable redundancy by using an N-1 measurement redundancy analysis principle for reference; step f, enabling the number of the PMU configuration to be as small as possible and the number of N-1 voltage phasor solvable redundancy nodes to be maximum under the maximum operating condition and under the guarantee of satisfying direct solvable power flow equations under different working conditions; and step g, rapidly searching a Pareto-optimal solution of multiple objective functions in the whole solution space by utilizing the global optimization capacity of a quantum genetic algorithm.

The invention discloses a multi-target intelligent power distribution network self-healing recovery method based on a quantum genetic algorithm. The method uses a node-layered forward and backward substitution method for flow calculation and uses a quantum genetic algorithm for self-healing recovery reconstruction of multiple targets of a power distribution network. The quantum genetic algorithm utilizes qubits to encode chromosomes and uses quantum revolving doors to adjust the chromosomes, so that in a relatively small population scale, the algorithm quickly converges to a global optimal solution. Island determination in the reconstruction is achieved by means of flow calculation, and the dimension of an infeasible solution is lowered. With power distribution network losses and switch action frequency as reconstruction targets, the method achieves comprehensive optimization of multiple targets and has practical value.

The invention discloses an optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method. The method comprises the steps of firstly performing modeling based on a photosynthetic rate of a GA-GRNN, and optimizing an expansion speed of a GRNN by utilizing a GA algorithm, wherein related analysis of a predicted value and an actually measured value of a photosynthetic rate prediction model of the GA-GRNN is remarkably superior to that of a GRNN model; and then based on the photosynthetic rate prediction model of the GA-GRNN, performing photosynthetic rate optimization by using a quantum genetic algorithm to obtain corresponding optimal light quality and photon flux density, and building a light environment control target value model by adopting multiple linear regression fitting, wherein determination coefficients of optimal light quality model and photon flux density model are 0.992 and 0.9893 respectively. The photosynthetic rate at each temperature is taken as the actually measured value, and the photosynthetic rate corresponding to the optimal light quality and the optimal photon flux density is taken as the predicted value; a related analysis method is adopted; the determination coefficient is 0.936, the fitting straight slope is 1.012, and the intercept is 0.054; and a result shows that the built coupled light quality and photon flux density control target value model is good in performance.

The invention relates to an alternating-current / direct-current-system reactive power optimization method based on a good-point set quantum particle swarm algorithm. The method comprises the following steps of step1, establishing an alternating-current / direct-current-system reactive power optimization model; step2, improving a quantum genetic algorithm; and step3, using the improved quantum genetic algorithm to solve the alternating-current / direct-current-system reactive power optimization model. In the invention, the QPSO is improved and a problem of premature convergence generated because the QPSO is easy to fall into local optimum is solved.

The invention relates to a multipurpose optimization method for a double closed-loop speed governing system of a brushless DC motor. Based on the quantum genetic algorithm, the multipurpose optimization for the double closed-loop speed governing system of the brushless DC motor can be realized. In this way, the problem in the prior art that the manual setting operation is time-consuming, labor-consuming and large in error can be effectively solved. Meanwhile, the prematurity and the local convergence ability of conventional algorithms are poor can also be overcome. In addition, a multipurpose fitness function is designed. Therefore, the damage to components caused by the large amount of controlled variables can be effectively prevented, so that an ideal speed governing system can be obtained.

The invention discloses a non-invasive electric appliance load identification method based on quantum genetic optimization. Specifically, an actually-measured load current and voltage data are used, acurrent effective value is obtained, the optimal solution is obtained through comparison and optimization through a quantum genetic algorithm, and finally the specific load type of an electric appliance is determined. According to the non-invasive electric appliance load identification method based on a genetic optimization identification algorithm, a quantum genetic optimization electric appliance identification algorithm is applied to non-invasive electric appliance identification technology, so that the number of solution spaces for finding the optimal solution is increased, the accuracy rate of simultaneous operation of multiple kinds of equipment is improved on recognition results, and meanwhile the time complexity is also reduced.

The invention belongs to the technical field of network information, in particular to a network security situation awareness method for a power distribution communication network. The method comprisesthe following steps: dividing data of the power distribution communication network into real-time data and historical data, collecting the data by event triggering and periodic collection separately,and uploading the data; performing duplication removal on the collected data by fingerprint extraction algorithm and simhash dimensionality reduction, performing denoising by proximity algorithm, organizing different features of the data into a vector form, and distinguishing the real-time data from the historical data by status bits; performing data fusion and situation assessment on the real-time data from the historical data by a Bayesian network security situation awareness model and a ball vector machine classifier model with parameters optimized and trained by quantum genetic algorithmseparately; and performing security prediction on the power distribution communication network, and visually displaying the security situations, including real-time security situation prediction, real-time situations, history and other information.

The invention discloses a local guiding trajectory planning method and device for a tractor automatic driving system. The local guiding trajectory planning method comprises the steps that 1 a local guiding trajectory is set as a B-spline curve, and coordinate parameters of six control points of the local guiding trajectory are set based on the B-spline curve; 2 the trajectory function of the localguiding trajectory is acquired according to the coordinate parameters of six control points and a B-spline curve expression; and 3 the coordinate parameters are optimized based on a quantum genetic algorithm to acquire the optimal local guiding trajectory.

The invention discloses a method and a system for optimizing a goods loading three-dimensional layout based on a quantum genetic algorithm and belongs to an intelligent loading optimizing method. The method mainly comprises the following nine steps: inputting information about counters and packing boxes, initiating, calculating suitability, mutating, crossing, measuring quanta, optimally retaining, selecting a wheel disc, judging terminal conditions and outputting an optimizing result. The variety of genetic population genes is effectively increased through the combination of quantum calculation and the genetic algorithm, and the maintenance capacity and the overall optimizing capacity of the system are accordingly improved, so that a more accurately optimizing result about the goods three-dimensional layout is obtained. Meanwhile, the operation steps of the method provided by the invention for optimizing the goods loading three-dimensional layout based on the quantum genetic algorithm are clearer than those of the traditional algorithm. The method and the system can be used for electronic fine management over all kinds of warehouse logistics industries and can be applied in a wide range.