49results about How to "Global convergence" patented technology

The invention relates to a feedback fuzzy inference-based unmanned plane real-time route planning method. The method comprises the following steps of: 1, updating a mode of the unmanned plane route; 2, establishing a fuzzy rule base; 3, acquiring a fuzzy antecedent; 4, designing a feedback fuzzy inference method; 5, defuzzifying; 6, determining other parameters; 7, determining the next position of the unmanned plane; and 8, completing the design. Compared with the conventional unmanned plane real-time route planning method, the method ensures that the unmanned plane has primary independent studying capability by applying the feedback fuzzy inference method so as to improve the route planning effect. The real-time route planning method designed according to the method has the advantages of short route, smooth path, global astringency and easy implementation, and is a more practical and reliable unmanned plane real-time route planning method for engineering application.

The invention discloses a multichannel seismic record reflection coefficient sequence simultaneous inversion method. The method thoroughly considers sparse characteristics of a formation reflection coefficient sequence and spatial transverse continuity of seismic channels, a parameterized exponential function is used to measure the sparsity of the reflection coefficient sequence, and a corresponding constrained optimization objective function is built. Firstly, based on the characteristics of a convolution model and the sparsity hypothesis of the reflection coefficients, on the premise of notlosing any information, a dimension reduction preprocessing matrix is designed, and the dimension of the constrained objective function is reduced; and then, a trust region sequential quadratic programming algorithm is used for high-efficiency solution. The algorithm has global convergence and a super linear convergence rate, the calculation efficiency is high, and the inversion precision is high.Compared with the traditional single-channel seismic record deconvolution algorithm, the algorithm of the invention has a more stable and reliable inversion result as the spatial correlation of the seismic channels is considered.

The invention provides a method and device for establishing a full-waveform inversion model for geophysics parameters. The method comprises following steps: residual values of real wave-fields and initial model observation wave-fields are calculated; by use of the residual values, gradients of an objective function to model parameters are calculated through an adjoint state method; A Hessian matrix is calculated by use of variations of the model parameters and variations of the gradients By use of the gradients and the Hessian matrix, trust region sub-problems are established through performing second-order expansion calculation to the objective function according to Taylor's formula; the trust region sub-problems are solved through a conjugate gradient method to obtain solutions, namely the probe step length of the next iteration, of the trust region sub-problems; updates of the model parameters and trust region radii are performed on the basis of the trust region algorithm to obtain the full-waveform inversion model of geophysics parameters. By use of the method and device, a high fidelity velocity model is obtained.

A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms comprises the steps of establishing a micro-grid grid-connected model including an energy storage device; according to the actual situation, establishing an optimal scheduling objective function at the smallest total micro-gird power generation costs and environmental pollution control costs; establishing operating constraints in a micro-grid system, and separately establishing system power balance constraints, storage battery charge and discharge power constraints, micro-power output power limits and electricity purchasing and selling constraints for interaction between the micro-grid and large grids; improving the standard particle swarm optimization; separately improving the inertia weight and acceleration factors; and proposing to use the sub-gradient optimization method to update the velocity of the particles in the particle swarm optimization. According to the micro-grid grid-connected optimal scheduling method based on the improved subgradient particle swarms, while the micro-grid grid-connected optimization involving the energy-storage device is solved, advantages such as a good optimization searching effect and a fast convergence speed are realized.

The invention provides a single-piece discrete type production operation scheduling optimization method. The method includes the following steps that 1, an objective function of a quality-date of delivery-cost collaborative optimization model is determined; 2, the objective function determined in the step 1 is converted into a fitness function of a genetic algorithm, a single-piece discrete type production system simulation model is established through simulation software, and an optimal plan for production operation scheduling is sought out through the genetic algorithm. In combination with the characteristics of single-piece discrete type manufacturing industry production operation scheduling, an improved quality loss function is applied to collaborating the complex relation among different indexes of quality, the date of delivery and cost, the quality-date of delivery-cost collaborative optimization model is established and solved through the integrated system simulation technology and the genetic algorithm, and obtained solutions are distributed more uniformly.

The invention relates to the technical field of underwater positioning, in particular to a single beacon positioning method of an underwater vehicle. According to the single beacon positioning methodof the underwater vehicle with global convergence, an underwater vehicle is provided with a hydrophone, a Doppler velocimeter, a depth meter, an attitude heading reference system and a GPS; and underwater acoustic beacons periodically broadcast underwater acoustic signals. According to the method, a nonlinear single beacon positioning model in a discrete state is converted into a linear time-varying model through state augmentation; when the underwater acoustic signal is not received, the relative speed and the attitude of the underwater vehicle and water by a device carried by the underwatervehicle are obtained to carry out dead reckoning; after the underwater acoustic signal is received, the underwater acoustic signal transmission time is collected through known underwater acoustic signal transmission time and is taken as an observation variable, and dead reckoning data and observation data of various sensors are simultaneously integrated to predict and update the single beacon positioning based on Kalman filtering. On the premise of meeting the observability of a positioning model, the method has global exponential convergence.

The invention provides a method for realizing low power consumption of an electric terminal. The method comprises the following steps: A, electrifying the terminal, and electrifying and initializing function modules; B, initializing parameters of a low-power-consumption model and monitoring the state and the electric quality of the terminal; C, judging whether the function modules need to work continually or not by a core plate; D, comparing residual electric quality with electric quality calculated by the model to obtain electric quality deltaE consumed by time deltat; E, controlling a power switching-on/off circuit of the corresponding function module which does not need to work by a low electric level signal output by a GPIO (General Purpose Input/Output) port and switching off the power supply of the module; otherwise, controlling the power switching-on/off circuit of the corresponding module by a high electric level signal output by the GPIO port and switching on the power supply of the module; and F, repeating the steps until all the modules are at an idle state. The method does not depend on information of system parameters and has strong adaptability; potentials or other related quantities of all the states do not need to be calculated, and a calculation amount and a needed storage space are small so that the instantaneity of an algorithm can be effectively improved; the method has overall convergence, the inherent limitation of a gradient method is overcome and the application effect of the algorithm is guaranteed.

The invention relates to a method for optimizing allocation of emergency resources in a chemical industrial park. On the basis of the traditional hazard source grading model, a hazard source risk dynamic evaluation model is established by combining characteristics of the chemical industry park and considering a domino effect of the accident and risk factors of a process flow and a grading result is obtained based on a pedigree clustering method. On the basis of the hazard source risk grade, a hazard-source-risk-grade-based multi-target optimization emergency resource allocation model is establishing by considering the rescue timeliness and economic applicability of configuration decisions. With the genetic algorithm optimization method, the multi-target emergency resource allocation modelis optimized and then a configuration strategy meeting an expected target is obtained finally. According to the invention, in order to solve some problems in emergency resource configuration optimization, a configuration optimization method with high global optimization capability is put forward. The optimization method has characteristics of having high openness, robustness, parallelism, flexibility, and globally convergence and having no special requirement on the mathematical forms of questions.

The invention relates to a production scheduling optimization method for a beer saccharification process, wherein beer enterprises are not ideal in scheduling effect because of formulating production scheduling schemes by virtue of artificial experience presently. The production scheduling optimization method disclosed by the invention comprises the following steps of: establishing a production scheduling mathematical model on the basis of a technological process mechanism and first, and then converting to a discrete form capable of solving by virtue of an ant colony optimization algorithm, and finally determining a production scheduling method for the beer saccharification process. The invention discloses a saccharification production scheduling method with a high global optimization capacity aiming at some problems in optimized scheduling for beer saccharification production, and the optimization method has the characteristics of openness, robustness, parallelism, global convergence, and no special requirements on problematic mathematical forms, and the like.

The invention relates to a scheduling and optimizing method for beer production. At present, the production scheduling of a beer producer still relies on the method featuring workers' experience. Despite the fact that this can meet the requirement for production, the storage amount of intermediate products is usually not controllable due to the high consumption of raw materials, low utilization efficiency of equipment and liquid cleaning. The invention comprises the following steps: obtaining the performance indexes of beer producing equipment; according to a production plan, determining a production scheduling target; according to the performance indexes of beer producing equipment and the production scheduling target, establishing a beer scheduling and producing model based on discrete time expression; using a self-adaptive collaborative optimization algorithm to optimize the beer scheduling and producing model; and finally, solving a production scheduling scheme. The method of the invention has the characteristics of openness, robustness, global convergence, and high efficiency and can achieve accurate and reliable optimization results.

The invention discloses a conveying path combination optimization method based on a stage structured predator-prey model. Supposing that multiple animal populations grow in an ecological system, and each population is divided into two stage types of adult populations and young populations; the adult populations produce their young populations at a certain birth rate, and the young populations grow up into the adult populations after a period of time; the influence of mutual competition for living resources between the populations is shown in the influence on the characteristics; the population having larger proportion in all the populations has higher influence, and the population spreads its influence to other populations; the advantageous and strong populations spread their high quality characteristics to other populations; and if one population is continuously influenced by other populations, its growth state continuously changes, and the global optimal solution of the problem of conveying path combination optimization can be rapidly determined by using the change and the multi-population predator-prey system model having the stage structure.

The invention discloses a multi-population competitive multi-objective station location combination optimization method with a protection region. It is assumed that a plurality of biological populations live in an ecosystem. The ecosystem is divided into two regions of a non-protection region and a protection region. If the density of some populations in one region is overhigh, the populations will automatically migrate to the region with the relatively low density, and therefore, the populations living in the low density region will be influenced by the migrating populations; the greater the proportion of one population in all populations is, the greater the influence of the population on the ecosystem is; one population becomes stronger, the advantage of the population will be overflowed to the other populations; the populations in the regions compete with and influence on each other; if one population is continuously influenced by the other populations, the growth state of the population will be continuously changed; and through utilization of the changes and a protection region population migration model, the global optimum solution of a multi-objective station location combination optimization problem can be determined rapidly.

The invention discloses a plague epidemic model-based multi-target transmission path combinatorial optimization method. In case that an epidemic which is called as plague exists in an ecological system formed by a plurality of persons, the epidemic propagates in the persons; the persons that are not infected with the epidemic are called easy-to-infect persons; the persons that are infected with the epidemic and do not put up symptoms in the easy-to-infect persons are called as exposed persons; after a period of time, the persons that start to put up symptoms in the exposed persons are called as diseased persons; the exposed persons and the diseased persons propagate the viruses in the bodies to the other persons; the persons that are cured in the exposed persons and the diseased persons are called as cured persons; in order to prevent the damage caused by the plague to the persons, the persons are vaccinated at set intervals; and under the action of the plague epidemic, the growth state of each person is randomly converted among four states such as an easy-to-infect state, an exposed state, a diseased state and a cured state, and by utilizing the random conversion and a plague epidemic model, a global optimum solution for a multi-target transmission path combinatorial optimization problem can be rapidly obtained.

The invention relates to the technical field of underwater positioning, in particular to a single beacon positioning method of an underwater vehicle. According to the single beacon positioning methodof an underwater vehicle with global convergence, an underwater vehicle is provided with a hydrophone, a Doppler velocimeter, a depth meter, an attitude heading reference system and a GPS; and underwater acoustic beacons periodically broadcast underwater acoustic signals. According to the method, a nonlinear single beacon positioning model in a discrete state is converted into a linear time-varying model through state augmentation, Kalman filtering prediction is performed while the relative speed of the underwater vehicle and the water and the attitude heading of the vehicle under a body axessystem; and when ocean current speed observation, depth meter observation and underwater acoustic signal transmission time are obtained, the ocean current speed, depth and underwater acoustic signal transmission time are updated through Kalman filtering. On the premise of meeting the observability of a positioning model, the method has global exponential convergence.

The invention discloses a permanent magnet motor optimization design method considering disturbance type design variable intervals in the field of motor optimization design. Firstly, design variables of a permanent magnet motor are constructed, corresponding optimization objects, optimization models and constraint conditions are determined, then initial intervals of the design variables are determined, optimal disturbance intervals of the design variables are determined by the aid of the constraint conditions, finally, optimal values of the design variables and optimal values of the optimization objects are obtained through joint simulation of software, and final optimization models are obtained. The method aims to solve the problem of disturbance of the design variable optimization intervals in the determination process and considers the problems of the effect of initial intervals with larger design variables on optimization efficiency and the accuracy of design variable results, through limitation on the constraint conditions, the optimal intervals of the design variables are determined, multiple optimization objects are weighed by the constructed optimization models, so that comprehensive optimal solutions are obtained, the design cycle is shortened, the design cost is reduced, and the method has the advantages of being simple to operate and effective in work.

The invention relates to a dynamic early warning method for a major hazard source of a chemical industrial park. The dynamic early warning method is characterized by adopting parallel coordinates to perform correlation analysis on correlated variables of key link parameters in the major hazard source, using screened variables with strong correlation as input variables, performing dimensionality reduction on the variables with strong correlation through a principal component analysis method before prediction; dynamically predicting a key production link variable through a recurrent neural network; and performing danger level evaluation by using dynamic prediction results of the key parameters. The dynamic early warning method of the invention is provided for solving some problems in dynamicearly warning of the hazard source of the chemical industrial park, and has the advantages of openness, robustness, parallelism, flexibility, no special requirement on mathematical form of the problem, and so on.

The invention discloses a KM (Kermack-Mckendrich) infectious disease model-based function optimization method, i.e., an SIR (Susceptible-Infective-Removed) algorithm. Based on an KM infectious disease model, suppose that N biological individuals exist in a certain ecological system, and each biological individual is represented by n characteristics; the ecological system has an infectious disease and the infectious disease is infected among the N biological individuals; the biological individuals exchange information with one another through an infection operator, a pathological operator, a cure operator, an immune operator and an activity operator; the individual with high PPI (Population Physique Index) transfers information on strong characteristic to the individual with low PPI through the pathological operator and the immune operator, so that the individual with the low PPI index can grow towards a good direction; one individual acquires average characteristic information of some other individuals through the infection operator and the cure operator, so that the probability that the individual gets in locally optimal solution is reduced; the search scope is expanded by improving the activity of the individual through the activity operator; and the algorithm has the characteristics of strong search capacity and global convergence and provides a method for solving a complex function optimization problem.

The invention discloses a neurodynamic method for solving a noise-containing time-varying problem. The method includes the following steps that: 1) the time-varying quadratic programming problem modelof an actual physical system is established; 2) optimal value optimization is performed on the time-varying quadratic programming problem model, partial derivative information about an optimal solution and Lagrangian multiplier are obtained; 3) the partial derivative information is transformed into a standard time-varying matrix form; 4) a deviation function is designed; and 5) a power type parameter-varying recursive neurodynamic method and four common activation functions are used to design a power type neurodynamic method of the noise-containing time-varying problem in the real-number field, and an obtained solution is the optimal solution of the time-varying quadratic programming problem. According to the neurodynamic method of the invention, on the basis of the parameter-varying recursive neurodynamic method, a global convergence characteristic is realized when the common monotonically increasing odd activation functions are adopted to solve the noise-containing time-varying problem, and error can converge to zero at a superexponential velocity, and therefore, calculation speed can be greatly improved.

The invention relates to an integrated optimization method for production planning and scheduling under uncertain requirements. At present, a production plan and scheduling problem solving mode is mainly a traditional optimization method, an optimization result is difficult to be an optimal solution, and the traditional optimization method cannot be realized technically, so that expected production targets cannot be realized, resource distribution cannot be issued, and the like. The method comprises the following steps: acquiring specific data of a production planning layer and a scheduling layer; respectively establishing a planning layer cost model and a scheduling layer cost model under the condition of uncertain demand according to the specific data; and utilizing an improved collaborative optimization method to optimize the plan and scheduling cost model under the condition of uncertain demand, and finally obtaining a production scheme with the lowest cost. Aiming at some problemsin production planning and scheduling optimization, the invention provides an optimization method with stronger global optimization capability, and the optimization method has the characteristics ofopenness, robustness, parallelism, global convergence, no special requirement on the mathematical form of the problem and the like.

The invention relates to a method for optimizing a beer production formula. A conventional production formula is determined by a method which combines human experience and tests. The method comprises the following steps of: obtaining main raw materials in a beer formula; establishing a comprehensive production performance index estimation model according to the parameters of the raw materials, wherein main considered comprehensive performance indexes comprise saccharifying power, wort total nitrogen, wort alpha amino nitrogen, wort beta glucan and the thickness of a distilled grain layer of a filter tank; and optimizing a beer formula model by using a variable-metric ant colony optimization method and finally obtaining a production formula with the lowest cost. The method of the invention has the advantages of openness, robustness, parallelism and global convergence.

The invention relates to a hybrid optimization method and system for auxiliary edge calculation of an unmanned aerial vehicle, and the method comprises that: taking the minimum energy consumption required by an auxiliary edge calculation model of the unmanned aerial vehicle as a target fitness function; performing iterative optimization solution on a target fitness function by utilizing a probability selection strategy of a hybrid artificial bee colony algorithm taking an imitated flower pollination algorithm framework as a reference, and judging whether a premature convergence phenomenon exists based on individual fitness value change and population diversity, so that the global optimization capability of the hybrid optimization algorithm is stronger, and then optimal calculation task unloading, calculation resource allocation and unmanned aerial vehicle deployment position schemes are realized. The method is high in calculation speed, high in calculation precision and suitable for solving the edge calculation service problem of large-scale mobile user equipment calculation unloading and task allocation.

The invention provides an improved particle swarm optimization-based economic load dispatching method for a power system, relates to the technical field of power systems, and aims at solving the technical problem of optimization of economic load dispatching of the power system. According to the method, a swarm management rule is introduced into a particle swarm optimization algorithm to form an effective swarm utilization strategy-based particle swarm algorithm; the quantity of particles is effectively changed through a global optimal value change of the swarm; the improved algorithm is high in convergence rate; and the convergence precision is high. The method provided by the invention is applied to economic load dispatching of the power system comprising different units, achieves performance improvement of economic load dispatching of the power system and obtains a good optimization effect.

The invention discloses a reservoir operation chart optimization method based on dynamic planning. The method comprises the following steps that: dividing a whole operation period into T time periods; separating an operation line, and dispersing the operation line in a feasible water level range of each time period to obtain water level discrete points; establishing all water level discrete point combined spaces Ct<k> of the operation line, and determining a reservoir operation chart recursive computation model based on the dynamic planning; from the Tth time period, beginning to carrying out inverse time sequence recursive computation; enabling t to be equal to t-1, entering computation of a next time period, and when the computation of a (t-1)th time period is finished, ending an inverse time sequence recursive computation process; and from the first time period, carrying out recursion to the last time period in the time sequence, and determining an optimal discrete water level combination (Ct) to obtain an optimal reservoir operation chart. By use of the method, a reservoir operation chart drawing model is coupled with a dynamic planning model, an inverse time sequence recursive way is adopted to calculate the optimal reservoir operation chart, and the global convergence of the dynamic planning is fully utilized for guaranteeing the global optimality of the obtained operation chart.