3411 results about "Local optimum" patented technology

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

A task scheduling method based on an improved chimp optimization algorithm, relating to the technical field of task scheduling, and comprising: acquiring a task to be scheduled and a task scheduling model established in advance by using a chimp optimization algorithm, and using a two-dimensional function sequence Halton to initialize a population and parameters; performing iterative computation on chimps in the task scheduling model by means of the chimp optimization algorithm; and when an iteration termination condition is reached, ending iterative computation, outputting an optimal solution, and obtaining an optimal scheduling scheme. According to the solution, the problems in the prior art of a traditional chimp optimization algorithm being prone to falling into local optimization, and the global exploration capacity and the local mining capacity being not balanced are solved, and the improved chimp optimization algorithm has performance improvement in different aspects compared with general population intelligent algorithms, and also achieves remarkable effectiveness in task scheduling applications in an actual cloud computing environment.

The present application discloses a task scheduling method based on an improved chimpanzee optimization algorithm, including obtaining a task to be scheduled and a task scheduling model pre-established by using a chimpanzee optimization algorithm, performing iterative computation of chimpanzees in the task scheduling model by the chimpanzee optimization algorithm; and ending the iterative computation in response to that an iteration termination condition is reached, outputting an optimal solution, and obtaining an optimal scheduling scheme. The present application solves the problem of the traditional chimpanzee optimization algorithm in the prior art that is prone to falling into the local optimum, and the imbalance between the global exploration capacity and the local exploitation capacity, the improved chimpanzee optimization algorithm has different aspects of performance enhancement compared to general intelligence algorithms of population.

The invention discloses an intelligent construction method based on data driving, and relates to the field of intelligent construction, and the method comprises the steps: collecting multi-source heterogeneous data of an intelligent construction project; performing feature selection on the acquired multi-source heterogeneous data by adopting a recursive feature elimination (RFE) algorithm to obtain a feature subset reflecting project progress, quality and safety; according to the feature subset, a multi-target model used for predicting project progress, quality and safety is constructed through a support vector machine (SVM) algorithm; a non-dominated sorting genetic algorithm NSGA-II is adopted to solve the multi-target model, a simulated annealing strategy is introduced in the iterative search process of the NSGA-II to jump out of a local optimal solution, and an optimized Pareto optimal solution set is output; and selecting an optimal parameter combination according to the Pareto optimal solution set, and adjusting the BIM model of the current intelligent construction project by using the optimal parameter combination. In view of low multi-objective optimization efficiency in intelligent construction project management in the prior art, the optimal model parameters considering different management objectives are obtained, and the efficiency is improved.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention discloses a robot path planning and obstacle avoidance method combining an artificial potential field and reinforcement learning. The method comprises the following steps: carrying out sensor data acquisition and fusion; environment complexity evaluation is carried out, a multi-scale planning mechanism is established, and global planning and local planning are determined based on switching conditions; carrying out artificial potential field path planning based on global planning; carrying out reinforcement learning path planning based on local planning; and judging whether the target point is reached, and returning to the determined plan until the target point is reached. According to the invention, the data of the infrared sensor and the laser radar are fused, the complementary characteristics of the two sensors are fully utilized, the comprehensive perception of static and dynamic obstacles is realized, and the adaptability of the robot in different environments is improved. According to the method, through reinforcement learning, strategy optimization and uncertainty estimation, the obstacle avoidance capability of the robot in a dynamic environment is enhanced, and the local optimum problem of a traditional artificial potential field method is solved.

The invention provides a multi-modal large model task processing method, device and equipment based on reinforcement learning, and the method comprises the steps: employing a multi-modal large model, generating G groups of responses for multi-modal visual task data, carrying out the scoring of each group of responses, obtaining a reward value, carrying out the standardization of the reward value, obtaining a dominance score, building a strategy updating gradient through the dominance score, and carrying out the calculation of the strategy updating gradient. A final result is obtained after expectation reward maximization, model parameter adjustment and multi-round iteration, and an expectation reward objective function comprises an expectation calculation item and KL divergence constraint, so that a current strategy can be updated by comparing the performance of candidate strategies through group relative strategy optimization, and local optimum can be helped to be jumped out; the strategy updating amplitude is limited by the KL divergence constraint, and the model is prevented from violently changing in the optimization process, so that the training stability is improved; the dynamic strategy iteration allows the model to adjust the balance between exploration and utilization according to the learning progress on the basis of keeping the stability, thereby further ensuring the effectiveness and stability of strategy optimization.

The invention relates to the technical field of organic display data processing, in particular to an organic display interface multichannel signal modulation method, which comprises the following steps of: generating a standardized data set through gamma correction and gamut mapping, and inputting the standardized data set into time sequence, amplitude and frequency sub-models deployed in a federal learning framework; and respectively analyzing the pulse width-refresh rate relevance, the driving voltage-brightness nonlinear relationship and the conduction period-mobility dynamic response, generating an optimized weight coefficient, and fusing the optimized weight coefficient into a boundary constraint condition of a cross-sub-model. A migration rate parameter index, amplitude gradient calculation and duty ratio correction service of chain calling is constructed based on a service grid, a reinforcement learning dynamic routing strategy is combined to avoid resource competition nodes, and brightness error characteristics are periodically fed back to reversely optimize gradient updating. According to the method, the conflict between time sequence deviation accumulation and local optimum is effectively suppressed, the color gradation continuity, the dynamic range and the edge sharpness are improved, and efficient cooperative modulation of multi-channel signals is realized.

The invention discloses an unmanned aerial vehicle route planning method based on an improved grey wolf optimization algorithm. The method comprises the following steps: 1, environment modeling and parameter setting; 2, designing constraint conditions and a target function; 3, initializing an algorithm and generating a population; 4, improving an algorithm and updating a track; and step 5, iteration termination and result output. The flight path of the unmanned aerial vehicle is efficiently coded in a spherical vector coding mode, and the flexibility and the calculation efficiency of flight path representation are remarkably improved; by introducing the Levy flight strategy, the global search capability of the algorithm is enhanced, and falling into a local optimal solution is avoided; meanwhile, a parameter adaptive adjustment strategy is combined, so that the convergence speed and precision of the algorithm are remarkably improved. According to the method, the problem of flight path planning in a complex mountain environment and a threat area is effectively solved, a safer and more efficient flight path is planned for the unmanned aerial vehicle, and the method has higher practicability and reliability.

The invention discloses an unmanned aerial vehicle routing inspection path planning method considering risk levels, which comprises the following steps: 1) simulating an experimental map, and establishing a related mathematical model of a path planning algorithm; 2) using an improved BI-RRT * adaptive step length path planning algorithm to carry out primary measurement recording on the path distance between the inspection points of the unmanned aerial vehicle; 3) designing a grey wolf algorithm evaluation function, enabling the evaluation target to be comprehensive optimum of the inspection task point risk and the unmanned aerial vehicle inspection path cost, and enhancing the local optimum separation capability of the algorithm; and 4) carrying out secondary planning on the path according to the sequence to ensure that the path is optimal. According to the method, the routing inspection cost of the unmanned aerial vehicle is considered in the routing inspection process, and routing inspection points with high risk degrees are preferentially inspected; an IRRT *-Connect path planning algorithm based on environmental complexity is designed, and it is ensured that the unmanned aerial vehicle plans an optimal path with high efficiency when facing a complex and narrow area.

Disclosed in the present application are a multi-objective optimization method and apparatus for a transformer, and a storage medium. The method comprises: determining a plurality of objective functions, optimization parameters and optimization constraints, and establishing a multi-objective initial model according to the objective functions, the optimization parameters and the optimization constraints; and generating an initial population, and by taking the initial population as a target parent population, executing an iterative strategy until a preset number of iterations are reached, obtaining a parameter optimization result according to an updated target parent population, and obtaining a multi-objective optimization model according to the parameter optimization result and the multi-objective initial model. Compared with a conventional multi-objective optimization algorithm for a transformer, since an artificial solution is additionally provided in an initial population, the convergence speed and optimization precision of an algorithm are improved, premature convergence is overcome, and the algorithm is prevented from falling into local optima.

The invention discloses a multi-agent collaborative pursuit method based on an intelligent algorithm in a dynamic environment, and the method comprises the steps: firstly obtaining an image containing the state information of an obstacle, processing the image through employing a variational auto-encoder VAE, and obtaining the motion information of an unknown obstacle; secondly, obtaining a complete state vector of the intelligent agent according to unknown obstacle motion information and intelligent state information obtained through observation; and finally, acquiring a pursuit strategy through a pursuit reinforcement learning network, and training through a multi-agent double-delay depth deterministic strategy gradient MATD3 algorithm. And acquiring an escaper strategy through the escaper reinforcement learning network, and performing training through a near-end strategy optimization PPO algorithm to complete multi-agent collaborative pursuit. According to the method, the stability of the control strategy and the network is improved, the possibility that the intelligent agent control strategy falls into local optimum is reduced, and the problem of efficient pursuit of multiple intelligent agents in a complex dynamic environment is solved.

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 relates to the technical field of electric power system control, in particular to an electric power resource intelligent control system based on digital twinning, which comprises a data acquisition module, a digital twinning model module, an electric power dispatching module, a fault early warning module and a three-dimensional visualization module. According to the method, the load demand prediction model is established based on the hybrid RNN, the RNN and full-connection network hybrid neural network model is constructed, the historical time sequence, the time feature, the historical statistical feature and the similarity feature are learned, and the problem that the power load demand is difficult to predict due to renewable energy source grid connection and power demand increase is solved; a power dispatching decision is made through a multi-objective function optimization algorithm based on an improved particle swarm, the particle swarm optimization algorithm is improved by adding a variation strategy for high-fitness particles, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the practice process is solved, and therefore the problem that power dispatching of multi-objective optimization is difficult to achieve is solved.

The present invention relates to the field of array antenna synthesis, and in particular to a sparse planar array synthesis method. In the present invention, in order to balance the proportion of a development and exploration process, a nonlinear function is used to reconstruct an arithmetic optimization accelerator in an arithmetic optimization algorithm; the top three optimal individuals are used to replace the current optimal individual to perform exploration and development and an elite mutation strategy is introduced, so as to enhance the capability of the algorithm to escape a local optimum, and improve the convergence precision of the algorithm; and an adaptive matrix mapping rule is provided, the current array element distribution is determined, and if the current array element distribution does not meet the minimum array element spacing constraint, the current array element distribution is adjusted by means of an adjustment strategy, thereby guaranteeing the degree of freedom of array elements while avoiding an unfeasible solution.

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 code spraying identification optimal path planning method based on an intelligent optimization algorithm, and relates to the technical field of code spraying control, and the method comprises the steps: dispersing a code spraying region into a three-dimensional space, and constructing a dynamic environment model of marking parameter constraint; based on the code spraying task priority queue, a hybrid optimization algorithm is adopted to carry out collaborative optimization of marking parameters and path nodes, and an optimal path candidate set is generated; an obstacle avoidance model is constructed based on the optimal path candidate set, a collision-free path is generated, the pose of a nozzle is adjusted in combination with visual feedback, and an anti-interference code spraying action sequence is output; monitoring the operation feedback of the code spraying equipment, and carrying out the iterative updating of the marking parameter library and the algorithm rule. According to the method, the core advantages of multiple intelligent optimization algorithms are fused, a mixed collaborative optimization architecture is constructed, the limitation of a single algorithm in a complex scene is effectively broken through, and a traditional path planning algorithm is easily troubled by a local optimal solution and is difficult to meet multi-dimensional constraint conditions at the same time.

The invention provides an unmanned aerial vehicle three-dimensional path planning method based on an improved sparrow algorithm, and the method comprises the steps: carrying out the initialization of a population through fusing Tent chaotic mapping and Sine chaotic mapping, thereby improving the diversity of the population, and improving the search range of the population; secondly, introducing a butterfly optimization algorithm, updating the position of a discoverer in a sparrow search algorithm, and enhancing the search capability of the algorithm for a globally optimal solution; then, position updating is carried out on the participant by using adaptive T distribution, the adaptability and flexibility of the algorithm are increased, K-means clustering is introduced to select the alert, and the path planning capability of the unmanned aerial vehicle in a complex environment is enhanced; finally, in order to prevent the algorithm from falling into local optimum, optimal individual Gaussian disturbance variation is introduced, and the algorithm is prevented from falling into local optimum in the search process. A simulation experiment result shows that compared with a traditional sparrow algorithm, the improved sparrow algorithm can find a safe and feasible path, and has relatively stable optimization capacity.

The invention discloses a ship path dynamic planning method based on a collision risk field, and the method comprises the following steps: carrying out the grid division of a map of a navigation water area of a ship, and obtaining a grid map of the navigation of the ship; performing path search on the grid map by adopting a path planning algorithm to obtain an initial route of the ship; establishing a collision risk field model of the target ship according to real-time navigation parameters of the target ship, and substituting the coordinates of the central point of each grid in the grid map into the collision risk field model to obtain a collision risk value of each grid in the grid map; when the ship sails to the grid with the collision risk value larger than a set threshold value, the grid which is closest to the terminal point and has the collision risk value smaller than the set threshold value serves as a local optimal point, and the ship route of the ship is updated; and repeating the steps until the ship arrives at the destination.

The invention discloses a data access permission dynamic adjustment system, which belongs to the technical field of data management and comprises a data collection module, a feature extraction module, a risk assessment module, a strategy generation module, a decision optimization module, a verification adjustment module, a permission application module, a user feedback module, an anomaly detection module and a monitoring recording module. According to the method, the candidate permission strategies can be quickly generated, the real-time decision-making requirement can be met, meanwhile, more selection spaces are provided for subsequent selection, a user is helped to understand the basis for strategy generation more visually, the permission management strategies can be optimized and adjusted easily, the access path can be adjusted in real time, the flexibility of the system is improved, and the user experience is improved. Global search can be carried out in a complex permission space, local optimum is avoided, and access continuity and stability are guaranteed.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction model construction method based on signal decomposition and parameter optimization, and the method specifically comprises the following steps: S1, original wind power data processing: employing a self-adaptive noise complete set empirical mode decomposition algorithm (CEEMDAN) to decompose the original wind power data; according to the method, a hybrid model fusing a bidirectional gating cycle unit (BiGRU), a bidirectional time convolution network (BiTCN) and a multi-head attention mechanism (MHA) is constructed, an improved parameter optimization algorithm is designed, the capacity of the model for capturing wind power short-term fluctuation characteristics is enhanced, the parameter optimization efficiency is improved, the local optimum problem is effectively avoided, and the method is suitable for the wind power short-term fluctuation characteristic capturing capability. According to the method, the limitation in traditional feature extraction is effectively improved, high-precision and high-efficiency ultra-short-term wind power prediction is realized, a reliable basis is provided for optimizing a power generation scheduling strategy for a power system, and the method can be popularized and applied to multivariate time sequence prediction scenes such as wind speed prediction and photovoltaic power generation prediction.

The invention relates to an optimization method for improving a whale algorithm based on chaos multi-strategy fusion, and belongs to the technical field of mechanical engineering. The method comprises the following steps: firstly, constructing a tension / compression spring design problem model, secondly, fusing multiple improvement strategies to improve a whale optimization algorithm, then, optimizing a mathematical model by using the improved whale optimization algorithm, and finally, outputting an optimal variable combination and an optimal target value of a tension / compression spring model. According to the whale optimization algorithm, multiple improvement strategies are fused, the initial population quality and population diversity of the whale optimization algorithm are improved, the convergence speed and exploration capacity are improved, the situation that a stretching / compression spring design method is caught in local optimum is avoided, and the algorithm has better optimization accuracy when solving the stretching / compression spring design problem.

The invention discloses a sparse array antenna optimization method based on an improved zebra algorithm, is used for reducing the peak side lobe level of a sparse array antenna, and belongs to the technical field of radio frequency phased array antennas. The implementation process of the method comprises the following steps: firstly, adding a probability estimation model on the basis of an original zebra optimization algorithm, and converting a real number variable into a binary code; secondly, an adaptive T distribution disturbance strategy and a dynamic selection strategy are introduced, the global search capability of the algorithm is enhanced, the algorithm is prevented from falling into local optimum, and the convergence speed is effectively improved; and finally, an optimal solution is obtained through iterative optimization for a certain number of times. In order to verify the performance of the method, three groups of simulation experiments are carried out, and the result shows that compared with other optimization algorithms, the method can obtain a lower array antenna peak sidelobe level within shorter optimization time, so that the performance of the antenna is improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention discloses a flexible continuum robot motion control method based on an improved Kerr-Ren optimization algorithm. The method comprises a novel flexible continuum robot 3D printing piece structure and a closed-loop model-free control method based on the improved Kerr-Ren optimization algorithm. According to the method, the improved angle exenx optimization algorithm is introduced, the Henon chaotic mapping theory, the linear adaptive weight and the mirror reflection learning model are introduced on the basis of the original algorithm, the diversity of population initialization is increased through improvement, the optimization precision of the algorithm is improved, and the optimization precision of the algorithm is improved. The global search capability and the local search capability of the algorithm in each stage are balanced, the convergence speed is effectively improved, and the algorithm is prevented from falling into local optimum; through combination of the improved angle exid optimization algorithm and the closed-loop model-free control method provided by the invention, path planning and attitude adjustment of the novel flexible continuum robot are realized, and a guarantee is provided for high-precision control of the novel flexible continuum robot.

The invention discloses a tunnel section fitting method and device based on a laser radar point cloud, and a medium, and the method comprises the steps: scanning a subway tunnel through employing a laser radar, and obtaining the point cloud data of the subway tunnel; obtaining a tunnel trend by using a principal component analysis method, and performing coordinate transformation on the point cloud; obtaining point cloud slices along the tunnel trend; projecting the slice point cloud to a YOZ plane, constructing a polar coordinate system by taking the mass center of the slice point cloud as a circle center, and converting slice points to polar coordinates; uniformly dividing the section point cloud according to polar coordinate angles; and improving a random sampling consistency algorithm by using angle distribution, and realizing elliptic model fitting of the section point cloud. According to the method, the improved random sampling consistency algorithm based on polar coordinates is introduced, the problems that the section ellipse model is low in fitting efficiency, prone to falling into local optimum and the like are solved, the precision, reliability and efficiency of tunnel section fitting are improved, and optimal fitting of the tunnel section ellipse model is achieved.

The invention discloses a monitoring and simulation mutual feedback correction concrete dam structure parameter intelligent identification method, and belongs to the technical field of dam safety monitoring, and the method comprises the steps: S1, building a finite element model, and generating a parameter set X through LHS; s2, calculating a water pressure component to obtain a data set Y, and normalizing a sample S = [X, Y]; s3, dividing the training set and the test set according to a ratio of 4: 1, and training a CBLA model; s4, the HST is used for separating the water pressure component, and ICPKO is combined with CBLA for optimization; and S5, iteratively terminating the output of the optimal parameter, and performing finite element verification. According to the concrete dam structure parameter intelligent identification method based on monitoring and simulation mutual feedback correction, the problem that a traditional algorithm is prone to being caught in local optimum is solved, the efficiency and precision of gravity dam structure mechanical parameter feedback are improved, the optimal combination of deformation moduli of a dam body and all partitions of a dam foundation meeting the actual situation is successfully inverted, and the method is suitable for popularization and application. And a new technical means is provided for structural response analysis of the gravity dam in the operation period.

The invention discloses a water plant whole-process intelligent optimization scheduling method based on adaptive feedback control. The method comprises the following steps: S1, dividing into a low-demand stage, a medium-demand stage and a high-demand stage; s2, in a low-demand stage, a medium-demand stage and a high-demand stage, correspondingly obtaining real-time monitoring data sets of the water plant; s3, constructing a multi-stage evolutionary game model based on the water plant real-time monitoring data set; s4, generating a local optimal scheduling scheme of the current stage; s5, outputting a local optimal scheduling scheme according to the evolutionary game model by utilizing a self-adaptive feedback control mechanism; s6, performing global optimal scheduling of the system in each stage; s7, optimizing a scheduling scheme in advance; and S8, displaying the optimized scheduling effect through the integrated intelligent scheduling platform, monitoring the operation state of the whole water plant, and generating a scheduling report for reference of management personnel. According to the invention, the predictability and perspectiveness of the scheduling system are effectively improved through the prediction model, and it is ensured that the water plant is always in an efficient and safe operation state.

The invention provides an unmanned aerial vehicle cluster obstacle avoidance strategy generation method based on a double-delay depth deterministic strategy gradient (TD3). According to the method, a Markov decision model is constructed by combining task requirements, environmental conditions and unmanned aerial vehicle performance parameters, the Markov decision model comprises a state space, an action space and a reward function, and the model is trained through a TD3 algorithm. The method comprises the following steps: firstly, determining a target motion direction and density of an unmanned aerial vehicle cluster, and setting a minimum safety distance and an obstacle distance to ensure flight safety; and then, defining a reward function, and guiding the unmanned aerial vehicle to realize effective obstacle avoidance and formation maintenance in a complex environment. Compared with a traditional rule-driven and classical algorithm (such as geometric path planning and an artificial potential field method), the scheme based on the TD3 has higher adaptive capacity and flexibility, the strategy can be rapidly adjusted in a dynamic environment, and local optimum is avoided. Besides, the TD3 algorithm improves the stability and convergence of the strategy through delay strategy updating and experience playback, so that the unmanned aerial vehicle cluster keeps real-time obstacle avoidance and formation stability in a high-density scene. Experimental results show that the method can effectively avoid obstacles, maintain a preset formation and reduce energy consumption in a simulation environment, and the application potential of the method in unmanned aerial vehicle cluster cooperative tasks is proved. The method is suitable for multi-unmanned aerial vehicle cluster tasks in the fields of military, logistics distribution, environment monitoring and the like, and the task execution efficiency and safety can be remarkably improved.