1322 results about "Local optimum" patented technology

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention discloses an unmanned aerial vehicle path planning method based on multi-objective optimization and improved particle swarm optimization. The method comprises the steps of 1, constructing a three-dimensional space map model; 2, introducing a multi-objective optimization strategy, and designing an objective function by adopting a weighted objective optimization method for evaluating the advantages and disadvantages of each path; 3, initializing particles by adopting an improved RRT algorithm in combination with a Sobol low-difference sequence, calculating a fitness value of each unmanned aerial vehicle path, and recording an optimal solution; 4, introducing a dynamic inertia weight adjustment strategy, and dynamically adjusting the inertia weight according to the number of iterations; a dive search mechanism in an eagle search algorithm is fused, and a particle restart mechanism is introduced to avoid falling into local optimum; 5, judging whether the set number of iterations is reached or not; if yes, iteration is stopped, and the optimal route of the unmanned aerial vehicle is returned to the environment model; if not, iteration is continued, and the optimal air route is searched. The invention aims to improve the path planning efficiency and robustness of the unmanned aerial vehicle in a complex environment.

The invention discloses a double-arm collaborative planning method, system and device based on reinforcement learning and a medium, and belongs to the technical field of mechanical arm control. According to the current state in the state space, a control action is generated, and three-dimensional displacement increment instructions of the left arm end effector and the right arm end effector are obtained; after the three-dimensional displacement increment instruction is responded to and double-arm cooperative control is executed, a mixed reward function is calculated; experience enhancement processing is carried out on the execution track, target resetting is carried out on the failure track, pseudo target experience is generated, and the original experience and the pseudo target experience are stored in a playback buffer; and updating parameters of the strategy network and the Q value network according to the empirical samples in the playback buffer, and completing optimization of the double-arm collaborative trajectory planning strategy. According to the method, the problems of sparse reward and local optimum in two-arm collaborative planning are effectively solved by fusing maximum entropy reinforcement learning and an experience playback mechanism, and the training efficiency and the strategy generalization ability are improved.

The invention discloses a path planning method and system based on an improved A * algorithm and an improved DWA algorithm, and relates to the technical field of path planning, and the method comprises the steps: outputting a dynamically optimized heuristic function weight; generating a complete path of the current navigation task in combination with the heuristic function weight; executing self-adaptive smoothing processing, and outputting a smoothed global path; performing dynamic trajectory prediction according to the dynamic window parameters to generate a plurality of motion trajectories; performing comprehensive evaluation on each motion track, generating an evaluation value of each motion track, and selecting the motion track with the optimal evaluation value as a final track; and inputting the path coefficient recorded by the navigation task to the path coefficient optimization network based on the hierarchical attention mechanism. According to the method, the planning time of the A * algorithm in global path planning is shortened, the search efficiency of the A * algorithm in a large-scale scene is improved by optimizing the algorithm structure, and the problem that the DWA algorithm is prone to falling into local optimum is greatly solved.

A full-automatic packaging scheduling method based on a multi-objective optimization algorithm, which method relates to the technical field of intelligent production scheduling in packaging workshops. The method comprises: on the basis of work orders to be packaged of a production line, extracting production data of the production line, and initializing parameters; constructing a multi-objective optimization model of the packaging production line; to address the problems of premature convergence and susceptibility to local optima in an INSGA-II algorithm, using the INSGA-II algorithm to improve an initialization method, crossover and mutation strategies and crossover and mutation factors, and performing solving on the basis of objective functions such as minimizing the maximum makespan, minimizing the maximum energy consumption and minimizing the total machine load, so as to generate an optimized scheduling scheme; and starting packaging operations on the basis of the generated optimized scheduling scheme. The method can increase the unit-time production capacity of a production line, and effectively solve the problems in packaging production scheduling, thereby reducing the production costs of enterprises and improving the packaging production efficiency.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The invention discloses an optimization design method for improving the environmental performance of a building site, and relates to the technical field of site design. The method comprises the following steps: S1, collecting and preprocessing basic information of a building site; s2, building a multi-dimensional digital model of the building site; s3, site environment performance simulation and index quantification are carried out; s4, environment performance parameters are intelligently optimized; s5, verifying and finally determining an optimization scheme; and S6, optimizing effect tracking and dynamic adjustment. According to the method, adjustable variables of a building candidate model serve as genes, binary crossover and polynomial variation are simulated through iteration of 100-200 sets of initial populations, and a multivariable combination space is automatically traversed; according to the method, planning specifications and a cost threshold value are converted into algorithm constraints, it is ensured that an output scheme is compliant and economical, 3-5 groups of non-inferior solution schemes are screened out in 3-5 rounds of iteration, standardized values of environmental performance parameters after optimization are all larger than or equal to 0.7, local optimum is avoided, and optimization efficiency and scheme comprehensiveness are greatly improved.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention discloses a high-dimensional data feature selection method and system based on a multi-strategy improved whale optimization algorithm, and the method guarantees the uniform distribution of populations through a good point set initialization strategy, and solves a search blind area problem caused by conventional random initialization. A whale optimization and particle swarm optimization double-population cooperation mechanism is adopted, and dynamic balance of global exploration and local development is achieved; and a tangential flight disturbance strategy is introduced, so that the capability of jumping out of local optimum of the algorithm is effectively enhanced. Finally, binary feature selection vectors are output and directly applied to machine learning model training, the classification precision is remarkably improved in the fields of medical diagnosis, image recognition and the like, the calculation complexity is reduced, and an efficient and reliable solution is provided for high-dimensional data feature selection.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention relates to the technical field of deep learning hyper-parameter optimization, in particular to a multi-target hyper-parameter joint optimization method and system for a deep learning model. The specific implementation process comprises the steps of obtaining a current hyper-parameter of a deep learning model, generating a learning track vector, and performing utility prediction to generate a learning utility projection; on the basis of the dominating relationship between the learning utility projection and the current Pareto optimal leading edge, pruning the disadvantage training task, updating the Pareto optimal leading edge by using non-dominating sorting, and constructing a Pareto strategy network; and carrying out topology congestion degree analysis on the updated Pareto optimal leading edge, generating a leading edge exploration bias vector, inputting the leading edge exploration bias vector into the Pareto strategy network to update hyper-parameter codes, and entering the next round of iteration. According to the method, through the multi-objective optimization algorithm and in combination with the dynamically updated Pareto strategy network, the problems that the hyper-parameter optimization process is long in time consumption, low in efficiency and prone to falling into local optimum are effectively solved, and the optimization efficiency and the solving quality are improved.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention relates to a multi-task-point cruise path planning method and device for an unmanned aerial vehicle. The method comprises the following steps: acquiring flight data of the unmanned aerial vehicle in multiple flight stages; the flight data comprises corner data when the flight stage is a corner stage; constructing a flight energy consumption model according to the undirected graph, the flight data and the rotation angle data; the energy consumption of each task point in the cruising process of the unmanned aerial vehicle is obtained; performing iterative optimization on the energy consumption of the plurality of task points based on a wild dog optimization algorithm to obtain a path planning result, and controlling the unmanned aerial vehicle to fly according to the path planning result; energy consumption of different flight stages is quantified by constructing a flight energy consumption model, including energy consumption of rotation angle data in a rotation angle stage, so that the complex environment adaptability is improved, iterative optimization is performed on a path in combination with a wild dog optimization algorithm, the problems of inaccurate energy consumption modeling and easy falling into local optimum are solved, and the energy consumption modeling efficiency is improved. The method has the advantages that the energy consumption optimization efficiency and accuracy of path planning are improved, and the local optimum problem is avoided.

The invention discloses a complex network disintegration method based on evolution deep reinforcement learning. According to the method, an encoder-decoder model fusing a graph convolutional neural network and a deep Q network is constructed, and is used for efficiently extracting importance features of nodes in a complex network and realizing dynamic decision-making of a node disassembling sequence according to the importance features. In order to optimize model parameters and improve search capability, an evolutionary algorithm is introduced to perform global exploration on the model parameters, and the problem that a directional optimization strategy is easy to fall into local optimum is avoided. Meanwhile, deep mining is carried out on an evolution result in combination with a reinforcement learning strategy, the overall optimization process is accelerated, and advantage complementation of parameter evolution and strategy learning is achieved. Experimental results show that the method significantly improves the efficiency and precision of network disassembly while maintaining the robustness of the model, and has good practical value and wide application prospects.

The invention discloses a wireless power transmission coil optimization method and a related system, which can realize automation of coil structure parameters and multi-target global optimization by acquiring a coil geometric parameter space, constructing a comprehensive optimization function and performing iterative search in the coil geometric parameter space. The problems that in traditional design, the number of simulation iterations is large, design efficiency is low, local optimum is prone to occurring, and multiple performance indexes are difficult to balance are effectively solved, coil design efficiency and precision are remarkably improved, and dependence on artificial experience is reduced. Therefore, the number of manual intervention and simulation is remarkably reduced, the coil design efficiency is improved, and the prototype development period of the WPT system is shortened. Besides, iterative search is carried out in the parameter space, so that the whole design space can be effectively explored, a locally optimal solution trap can be jumped out, and a globally optimal or approximately globally optimal coil structure parameter combination can be obtained more possibly.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to the technical field of power distribution networks, in particular to an intelligent optimization method for energy storage capacity configuration in a new energy power distribution network and an application system. Comprising the following steps: data acquisition and preprocessing; building a multi-objective optimization model; and performing simulation verification and scheme output. According to the method, in a new energy power distribution network safe operation and complex working condition adaptation scene, the power grid safe operation constraint and the energy storage equipment physical characteristic constraint are integrated, so that the energy storage configuration scheme is ensured to accord with equipment physical safety and power grid operation criteria; meanwhile, a hybrid intelligent optimization method of improved particle swarm algorithm global exploration and interior point method local refinement is adopted, the solving precision is improved to avoid local optimization, multi-scene verification is carried out through a simulation environment consistent with the electrical characteristics of the actual power distribution network, the applicability of the scheme under the complex working conditions of severe fluctuation of new energy output and the like is ensured, and the method is suitable for popularization and application. The problems that a traditional scheme is insufficient in security constraint coverage and incomplete in solving and verification mechanism are solved.

The invention discloses a micro-grid group operation optimization method and system based on a multi-target dragonfly algorithm, and the method comprises the steps: constructing a system model of a micro-grid cluster, and enabling the system model to comprise a plurality of micro-grids; taking the connection state of the plurality of micro-grids as a decision variable to represent the communication mode between the micro-grids; a dynamic grouping mechanism is introduced, and the connection relation of the multiple micro-grids is adjusted according to actual requirements; establishing a multi-objective optimization model, and designing operation constraint conditions; combining decision variables and constraint conditions, designing a structure-scheduling joint optimization strategy, and carrying out collaborative optimization on a micro-grid group structure and a scheduling strategy; and according to an optimization result, outputting a Pareto optimization scheduling solution set covering different operation preferences, and obtaining a low-carbon economic multi-energy-flow cooperative operation scheme of the micro-grid group. According to the method, the problems of local optimum and low convergence speed during multi-objective optimization in the micro-grid dispatching process can be solved.

The invention discloses a wheeled robot path planning method for different scenes and a related device, and the method comprises the steps: obtaining environment data around a robot, building a global grid map, carrying out the preprocessing of the global grid map, carrying out the geometric feature recognition and angular point extraction of an obstacle, and fusing a multi-sampling strategy to generate a path search network. Path searching is carried out according to the target point and the pose planning, and a global optimal path conforming to robot driving is obtained; segments in the global optimal path are intercepted through the rolling window principle to serve as local paths to be input into a local path planning algorithm, a local optimal track is generated, and a speed instruction is output; and updating the map in the rolling window in real time, executing an obstacle avoidance task on the obstacle through the local optimal track, driving along the global optimal path again after obstacle avoidance, and moving to a target point according to a speed instruction. According to the method, the problems of large speed fluctuation, low quality of the generated path and calculation burden caused by increase of alternative path points in the current robot driving process are solved.

Methods and systems for tensor network contraction are provided. A method implemented by a computing host comprises obtaining a contraction tree associated with a tensor network, wherein a plurality of vertices and edges of the contraction tree correspond to a set of tensor nodes and indices of the tensor network, respectively; iteratively performing operations until a termination condition is satisfied, the operations including selecting a sub-graph of the contraction tree; replacing the sub-graph with a local optimal sub-graph; and obtaining an optimized contraction tree including the local optimal sub-graph; and outputting the optimized contraction tree.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, in particular to an improved sparrow searching method for three-dimensional unmanned aerial vehicle path planning. According to the method, a three-dimensional mountain terrain model is constructed through a Gaussian function superposition method, obstacles and no-fly zones are defined by adopting a cylindrical geometric model, and obstacle avoidance constraints, take-off and landing safety constraints and flight height constraints are set; a comprehensive cost function with path length, threat penalty, flight safety height and path smoothness as sub-targets is constructed, an improved sparrow search algorithm is introduced, and a sine and cosine strategy is adopted in discoverer position updating to dynamically balance horizontal exploration and vertical obstacle avoidance. A Levy flight mechanism is introduced in follower position updating so as to enhance the capability of local development and jumping out of local optimum; the method comprises the steps of population initialization, role division, iterative optimization, collision detection and an elitist retention strategy. According to the method, a safe and smooth three-dimensional flight path with the optimal comprehensive cost is output, and the method is suitable for an actual unmanned aerial vehicle navigation task.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and system for a power grid line, and particularly relates to the technical field of power grid routing inspection, and the method comprises the steps: 1, dynamically fusing a multi-source sensor and prior data, and constructing and updating a comprehensive threat field with a time attenuation mechanism and a passability map in real time; 2, decomposing an inspection line into subtask sections, and generating a reference route of each section by combining static safety cost; 3, on the basis of a real-time map in the flight of the unmanned aerial vehicle, a local optimal track is solved online through rolling window optimization of adaptive weight adjustment; and step 4, realizing multi-machine collaboration and automatic handover of blocked tasks through a block chain type distributed task state account book and an intelligent contract rule. According to the invention, the real-time avoidance capability, the environmental adaptability and the task continuity of the unmanned aerial vehicle to dynamic obstacles are remarkably enhanced, and the safety and the efficiency of power grid inspection operation are effectively guaranteed.

The invention belongs to the technical field of wind and light hydrogen storage resource allocation, and discloses a wind and light hydrogen storage scheduling method based on a sparrow search algorithm and related equipment. The wind-solar hydrogen storage scheduling method based on the sparrow search algorithm comprises the steps of constructing a wind-solar hydrogen storage operation day-ahead profit model according to a scheduling target of a wind-solar hydrogen storage system, obtaining a profit maximization objective function through the wind-solar hydrogen storage operation day-ahead profit model, and solving the profit maximization objective function by adopting the sparrow search algorithm. Obtaining a globally optimal solution, and scheduling the wind and light hydrogen storage system according to the globally optimal solution; the method can overcome the defects that in the prior art, the optimization speed is reduced during optimization scheduling, the randomness of an optimization scheduling result is high, and local optimum is likely to happen.

The invention discloses a heat transfer performance prediction and optimization method of a multi-dimensional data weighted neural network, and belongs to the field of artificial intelligence and process equipment enhanced heat transfer. The prediction method comprises the following steps: acquiring multi-physical field heat transfer data, preprocessing the data, and dividing the data into a training set and a test set; performing multi-channel independent normalization processing on the preprocessed data; establishing a neural network prediction model; globally optimizing the weight and the bias parameter of the neural network by adopting a genetic algorithm; using the optimized parameters to initialize a neural network to carry out refined training; evaluating the model performance by using the test set and outputting a heat exchange performance prediction result; and carrying out heat exchange characteristic analysis and equipment optimization design based on a prediction result. According to the method, neural network hyper-parameters are optimized through the genetic algorithm, the problem that a traditional neural network is prone to falling into local optimum is solved, high-precision prediction of heat exchange performance parameters is achieved, and reliable technical support is provided for design and optimization of efficient heat exchange equipment.

The invention discloses a permanent magnet synchronous motor parameter optimization method based on an improved moth flame particle swarm algorithm. In order to solve the problems that a traditional PID controller is difficult in parameter setting, poor in adaptability, insufficient in control precision and the like under complex working conditions, in the strategy, an MFPSO algorithm fuses a particle swarm optimization (PSO) algorithm and a moth flame optimization (MFO) algorithm. An adaptive inertia weight and Gaussian mutation strategy is introduced, so that particles can focus on local fine search, and the speed of convergence to an optimal solution is accelerated. The optimization capacity of the algorithm in different stages is enhanced by adopting a nonlinear acceleration coefficient, and local optimum is effectively avoided. A grouping evolution mechanism is designed to promote intra-group information sharing and inter-group competition, the population diversity is enriched, and the ability of the algorithm to jump out of a local optimal solution is further improved. The fuzzy PID controller based on the MFPSO algorithm optimization parameters can better meet the requirements of high-performance control of permanent magnet synchronous motors in different fields, and has application prospects and economic values.

The invention aims to provide an unmanned aerial vehicle operation airspace information processing method, a flight plan optimization method and a flight information management system, in the scheme, when an operation airspace topological graph is constructed, the calculation complexity is obviously reduced, the calculation efficiency is effectively improved, meanwhile, the connectivity of a key airspace is effectively reserved, and the structural interpretability is improved; when the flight plan is optimized, a double-layer optimization decision framework is adopted and comprises a strategy self-adaptive selection layer and a mixed integer nonlinear programming scheduling layer, so that optimization scheduling is more global, and local optimum is avoided; besides, the flight information management system effectively utilizes calculation results of related methods, airspace operation limitation information including an operation airspace topological graph can be obtained more efficiently and accurately, a flight plan is better optimized on the basis, the cooperative operation capability of the unmanned aerial vehicle and the manned aerial vehicle in the airspace is improved, and the flight efficiency is improved. And comprehensive operation situation information is provided for the controller to assist the controller in making an accurate decision.

The invention provides a flight path optimization method and system based on an improved particle swarm optimization algorithm, and belongs to the technical field of intelligent optimization algorithms and aircraft flight path optimizing.The method comprises the steps that concerned performance indexes in the flight process of an aircraft are obtained to construct a cost function; the cost function is sampled, sampling points of the cost function serve as particles, the improved particle swarm optimization is adopted to optimize the cost function, and a global optimal position is obtained and serves as a final track optimization result; the improved particle swarm algorithm comprises the following steps: carrying out particle initialization by adopting a method for assigning a particle initial value, carrying out random team distribution on each particle by adopting a Monte Carlo method, carrying out first-time updating on a global optimal particle with a minimum adaptive value by using a gradient descent method, and carrying out second-time updating on a global optimal particle with a minimum adaptive value according to the optimal position of each particle in the team and the optimal position of the particle in the team. And the speed and the position of the particle are updated for the second time based on the second-order consistency theory. According to the method, the convergence speed of the algorithm is improved, and falling into a local optimal solution is avoided.

The invention belongs to the technical field of computing resource allocation, and discloses a task-driven computing resource dynamic allocation system, which is characterized in that task differentiation features are extracted through a task feature intelligent analysis module, and global optimal allocation, 'task fingerprint 'matching and opportunity cost evaluation mechanisms are realized in combination with a reinforcement learning scheduling decision module. Mismatching conditions such as allocation of lightweight tasks to a high-video-memory GPU are avoided, meanwhile, the elastic resource pool dynamically adjusts the capacity, reduces resource locking during task pause, remarkably reduces resource waste, improves the overall utilization rate of computing resources such as the GPU, can quickly expand the capacity through the elastic resource pool to cope with burst tasks, and a cross-node resource cooperation module constructs a global view and improves the overall utilization rate of the computing resources. Efficient calling of idle resources is realized; a'logic super node 'and zero-aware migration technology breaks node barriers, avoids global redundancy caused by local optimum, enables resources to flexibly flow along with task loads, and improves the adaptive capacity of a system to dynamic tasks.

The invention discloses a dynamic order distribution optimization system based on deep learning, and relates to the technical field of order management, and the system comprises a multi-dimensional dynamic data collection module which is used for obtaining the multi-dimensional dynamic data of each service item in real time; the project emergency feature calculation module generates a project emergency feature value reflecting the resource allocation priority; the resource reachability feature extraction module outputs a resource reachability feature value reflecting the task assignment difficulty level; the composite dispatch risk feature fusion and optimization model module performs dynamic weighted fusion on the two types of feature values to generate a composite dispatch risk feature vector, inputs the composite dispatch risk feature vector into an asynchronous parallel genetic optimization model to perform dispatch path search, and introduces a convergence premature recognition mechanism to prevent the algorithm from falling into local optimum; and the dispatching strategy dynamic adjustment and feedback optimization module continuously optimizes model parameters through feedback of an actual service execution result to realize closed-loop iteration update of a dispatching scheme.