12results about How to "Avoid local convergence" patented technology

The invention discloses an oxygen-enriched bottom blowing copper matte grade prediction method based on a GA-BP neural network, which improves optimization efficiency and model prediction precision by improving a core mechanism of a genetic algorithm. The method comprises the following steps: collecting actual production data in a stable production period of a copper smelting plant; an existing GA-BP neural network is improved by adding a self-adaptive crossover probability adjustment mechanism and a self-adaptive mutation probability adjustment mechanism in a genetic algorithm, and the self-adaptive crossover probability adjustment mechanism not only depends on individual fitness, but also is combined with global and local information to be used for enhancing global search capability and local optimization capability. The adaptive mutation probability adjustment mechanism not only combines individual fitness, but also introduces global entropy and dynamic noise influence, and meanwhile, allows the mutation probability to be increased randomly in the later stage of evolution so as to jump out of local optimum; and by utilizing the improved GA-BP neural network, taking actual production data as input, predicting the oxygen-enriched bottom blowing copper matte grade and outputting a copper matte grade value.

The invention discloses a three-network emergency resource optimization method and system fused with an unmanned aerial vehicle communication relay, and belongs to the technical field of power system planning and disaster defense, and the method comprises the steps: building a communication power supply coupling model based on an unmanned aerial vehicle relay drive, and obtaining a communication recovery basic framework; constructing an outer layer optimization model taking the pre-disaster multi-type emergency resource layout scheme as a decision variable; an improved bald eagle optimization algorithm is adopted, constraint conditions of a communication recovery basic framework are embedded, an outer layer optimization model is iteratively solved, and a pre-disaster resource deployment scheme which is most robust in all scenes is determined; constructing an inner-layer model by adopting mixed integer second-order cone programming, performing refined solution on post-disaster emergency response, and optimizing a pre-disaster resource deployment scheme; and constructing a double-layer robust optimization model to realize three-network emergency resource optimization. According to the method, the cooperative scheduling capability of multiple types of resources in pre-disaster, in-disaster and post-disaster stages can be improved, the guarantee capability of key loads is enhanced, and the method has global search capability and engineering adaptability.

This invention provides a method for identifying parameters of a heat pump system based on an improved Adam co-evolutionary algorithm, belonging to the field of industrial control process system identification technology. It solves the technical problems of traditional algorithms in heat pump system parameter identification, such as lack of physical constraint guidance, easy output of meaningless solutions, slow convergence speed, and the tendency of co-evolutionary algorithms to get trapped in local optima. The method includes the following steps: Step 1) Constructing a Hammerstein-Wiener model of an air-source heat pump based on the thermodynamic mechanism of core components; Step 2) Constructing the identification process of the improved Adam co-evolutionary algorithm, combining three groups of cooperative search, chaotic mechanisms, and the Adam optimizer to achieve efficient and accurate parameter identification. This invention uses the improved Adam co-evolutionary algorithm for air-source heat pump system parameter identification, which has both fast convergence speed and high identification accuracy, effectively avoids local optima, and has small parameter estimation errors.

The present invention discloses an intelligent optimization method for a housing construction scheduling scheme, and belongs to the technical field of construction scheduling optimization. The method comprises the following steps: establishing a multi-objective particle swarm construction scheduling optimization model with the shortest construction period, the lowest construction cost and the highest construction quality as the objectives; adopting the Pareto principle, an external storage library and an adaptive grid to select individual historical optimal solutions and global optimal solutions; establishing a construction scheduling scheme to be optimized, solving the priority constraint problem, and improving the operability and universality of the above model in solving the intelligent optimization problem of housing construction scheduling schemes. The present invention has the advantages of high feasibility and strong ability to search for the optimal solution, and can provide decision makers with a reasonable housing construction scheduling optimization scheme to minimize the construction period and construction cost while ensuring the construction quality.

The present invention provides a deep-sea submersible flexible hand grasping operation path planning method, which belongs to the deep-sea submersible path planning technology, and specifically includes the following steps: initializing parameters; using random initialization to initialize the original population, using a data preprocessing method based on the kmeans algorithm to perform data preprocessing operations on the randomly initialized population; using the Q_learning algorithm to obtain the most suitable parameter combination for the operation of the particle swarm algorithm; calculating whether the number of consecutive unupdated particles is greater than a threshold, and replacing the component of the current particle in this dimension with the component of the global optimal solution in this dimension; using an improved simulated annealing algorithm strategy to update the global optimal solution in the population; until the requirement of the number of iterations is met, the optimal planning path of the flexible hand is output. The technical solution of the present invention overcomes the problem that the prior art cannot plan the grasping operation path of the submersible flexible hand in a deep-sea environment.

A transient voltage stability evaluation method based on a probabilistic neural network comprises the following steps: step 1, analyzing characteristics after wind power grid connection based on historical operation data of a power system to obtain system operation data samples, and classifying the samples according to critical fault clearing time margin values to form a corresponding initial sample set; 2, for the initial sample set, using a fusion feature t distribution-based random neighbor embedding algorithm to carry out dimension reduction processing on the feature quantity; 3, optimizing parameters of the probabilistic neural network by adopting a genetic ant colony hybrid algorithm; and 4, performing model training by using the training set as the input characteristic quantity of the optimized PNN to obtain an evaluation model, and finally performing transient voltage stability evaluation. According to the method, for security risk situation cognition, prospective early warning and formulation of prevention and emergency control measures, voltage instability and voltage collapse caused by the fact that large-scale new energy is merged into the power grid can be prevented.

The present invention belongs to the field of production scheduling, and specifically discloses a parallel batch processing machine scheduling method and system based on an adaptive differential evolution algorithm, which includes: S1 generates an initial population, and sets the number of iterations t=1; S2 randomly selects a target individual from the population; S3 selects the selection probability of a mutation operator, a scaling factor, and a crossover probability interval with equal probability or adaptively according to historical data according to whether the number of iterations t is within a preset learning period; S4 performs differential mutation and crossover on the target individual to obtain a test individual; S5 updates the individuals in the population based on the objective function value of the test individual; S6 repeats S2 to S5 until all individuals in the population are selected, and updates an external archive set based on the individuals in the population; S7 determines whether t reaches the maximum number of iterations: if so, the iteration ends, otherwise t=t+1 is set, and returns to S2. The present invention has the characteristics of quickly solving the multi-objective scheduling problem of parallel batch processing machines, improving machine utilization and reducing energy consumption.

The present invention provides a step time identification method based on broken line function fitting, comprising the following steps: (1) reading step response test data to obtain a set of disturbed data containing a time series; (2) constructing a broken line function; (3) solving the parameters of the broken line function using a genetic algorithm based on population fusion so as to minimize the data variance, and taking the moment at the turning point of the broken line function as the step time. The present invention identifies the step time by fitting a broken line function, thereby avoiding the error caused by manual interpretation based on experience, and can identify the step time in the presence of disturbed data, thereby achieving accurate interpretation of the test data and improving the accuracy of the test data interpretation.

The application discloses a dam slope anti-overtopping device, which belongs to the technical field of dam protection in water conservancy projects and comprises a plurality of protection sheets and a plurality of anchor nails. The plurality of protection sheets are laid on a dam slope, each protection sheet is in a wave shape and has equal peak and valley amplitudes, the protection sheet is inclined upward from a first end to form a first wave peak, the protection sheet is inclined upward from a last wave valley to form a second end, the protection sheet is gradually tilted upward from a third end to a fourth end, the first end and the second end of two adjacent protection sheets in a second direction are overlapped to form a continuous wave structure extending in the second direction, and two adjacent protection sheets in a first direction, the fourth end of the upper protection sheet is overlapped on the third end of the lower protection sheet. Each protection sheet is connected to the dam through the anchor nail at the wave valley of the third end. The wave-shaped protection sheet forcibly converges and disperses water flow, gradually overlaps to dissipate energy, and is anchored at a low wave valley, thereby realizing efficient water diversion and structural stability under the overtopping condition, achieving the protection effect of overtopping without collapse.

The present invention discloses a method for machine-based collaborative mapping in an unknown dynamic environment. The present invention combines the perception advantages of the wide field of view of aerial intelligent perception equipment and the complete details of ground intelligent perception equipment. First, the three-dimensional point cloud maps constructed by the two are pre-processed by dynamic point cloud filtering, noise point cloud filtering, ceiling and ground point cloud filtering, and out-of-field outlier point cloud group filtering to obtain a high-quality static point cloud map; then, the SAC‑IA algorithm based on the FPFH feature is used for coarse alignment, and then the ICP algorithm is used for fine alignment to improve the matching accuracy while preventing local convergence. The method of the present invention is 8% more accurate than the mapping accuracy of aerial intelligent perception equipment alone, and 51% more accurate than the mapping accuracy of ground intelligent perception equipment alone. It significantly reduces the mapping error, improves the perception accuracy, improves the system robustness, and achieves better perception effects in small and medium-sized indoor complex environments.

The present invention provides a step rise time identification method based on quasi-second-order response, comprising the following steps: (1) reading step test data to obtain a set of disturbed data containing a time series; (2) constructing a quasi-second-order response function; (3) solving the undetermined coefficients in the quasi-second-order response function using a genetic function so that the variance between the fitted function and the original data is minimized; (4) based on the obtained undetermined coefficients, taking the moment when the data on the quasi-second-order response function reaches a final value of 0.9 as the rise time. The present invention identifies the rise time by calculating the quasi-second-order response function, thereby avoiding errors caused by manual interpretation based on experience. The present invention is suitable for performance testing and parameter setting of various control systems.

The invention relates to the field of intelligent mobile carriers, in particular to a multi-mobile-carrier order operation control method, which comprises the following steps of simulating a real environment to construct a simulation environment and a mobile carrier model, setting a reinforcement learning network framework, and defining an observation space and an action space of each mobile carrier; and according to the mobile carrier formation and the comfort task, designing a time sequence reward function in stages, wherein the reward function comprises a mobile reward, a formation reward, a state reward, an acceleration reward and an angular acceleration reward. According to the method, a staged thought is adopted, a simple obstacle-free scene is gradually transited to a complex scene containing a fixed obstacle and a dynamic obstacle, and the adaptability and stability of the model in different environments are ensured; by designing award functions of a mobile award, a formation award, a state award, an acceleration award and an angular acceleration award, the mobile carrier can be guided to move towards the target point, and the comfort of passengers is improved while the efficiency is ensured.