160 results about "Global optimum" patented technology

The invention discloses a heterogeneous multi-agent entropy regularization resource allocation method for dynamic spectrum sharing, which belongs to the technical field of wireless network communication, and comprises the following steps: loading a basic trainer, and managing subsequent reinforcement learning; the method comprises the following steps: constructing a simulation environment, establishing a mathematical model, initializing parameter analysis, constructing a training environment and an evaluation environment, and creating an intelligent agent; generating initial empirical data by using a random strategy, filling an empirical playback buffer area, returning to a final state after a preset preheating step number is reached, and performing formal training to sample data from the empirical playback buffer area to update a network; starting a formal training process, interacting the intelligent agent with the environment, and storing empirical data to a buffer area; the strategy network is optimized through the strategy gradient, and the evaluation network is optimized through the TD error. By the adoption of the method, the problem of performance bottleneck caused by the fact that the strategy is converged to be suboptimal in spectrum allocation of a traditional DRL method is solved, and resource allocation efficiency close to global optimum is achieved through random strategy optimization.

The invention discloses an unmanned ship obstacle avoidance path planning method and system based on global optimum, and belongs to the field of unmanned ship navigation obstacle avoidance. Comprising the steps that S1, a kinetic model of the unmanned ship is constructed, a motion coordinate system is defined, and the unmanned ship has corresponding speeds in the surging direction, the swaying direction and the yawing direction; s2, improving and simplifying the dynamic model of the unmanned ship through the influence of the water environment influence factors on the dynamic characteristics of the unmanned ship; s3, formulating a collision avoidance strategy based on predictive control, performing discretization processing, calculating parameters encountering other ships and a collision risk, and determining a target function in the collision avoidance strategy; and S4, determining a final collision avoidance path method, and searching a path global optimal solution of the unmanned ship through a loop iteration mode. According to the technical scheme provided by the invention, the problem that unmanned ship path planning and obstacle avoidance cannot be accurately and efficiently carried out in the prior art is effectively solved.

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 an equipment abnormity monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance of the Internet of Things, and the method comprises the steps: collecting monitoring data to generate a high-dimensional original matrix, carrying out the optimization through employing a GCN model and combining with ACO, carrying out the updating through a comparison learning model and an FCM algorithm, and carrying out the searching of global optimum through employing a VAE model and combining with a PSO algorithm. The method comprises the steps of performing classification optimization based on K-means clustering and BSO, performing MLE calculation, updating dynamic causal KG through Granger causal test, generating a multi-modal result array through NSM, a scoring formula, a naive Bayesian model, a Mahalanobis distance formula and a logistic regression model, and performing optimization by using a fuzzy rule and GWO. According to the method, the GCN model is combined with the adaptive optimization algorithm, the precision and response speed of anomaly monitoring are improved, optimization is carried out by using the fuzzy rule and introducing the GWO based on multi-modal causal reasoning, and the reliability and efficiency of anomaly monitoring are improved.

The invention relates to a three-dimensional point cloud registration method and system based on a two-dimensional visual large model. A source point cloud and a target point cloud are decoupled into two-dimensional projection representation; constructing a structure consistency restoration domain, restoring geometric structure fracture and semantic deficiency in the projection image, and generating a dense and continuous restoration image; inputting the repaired image into a pre-trained visual geometry large model, carrying out cross-modal feature matching and pose resolving, and synchronously outputting a pixel-level matching confidence map; and inversely mapping a two-dimensional pose calculation result to a three-dimensional space to complete coarse registration, constructing a high-credibility anchor point set in the three-dimensional space by using a confidence map, and guiding a point cloud fine registration algorithm to converge to global optimum. According to the method, the registration large model for natural image training can be seamlessly migrated to the point cloud data, and retraining is not needed; in a weak texture and partially overlapped point cloud scene, a robust rough registration initial value can still be provided; the convergence speed and the anti-noise performance of fine registration are remarkably improved, and adaptive improvement from coarse to fine is achieved.

The invention relates to the technical field of railway line selection design, in particular to a railway line searching method, medium and equipment in a wide and dense obstacle environment. The method comprises the following steps: acquiring obstacles passed by a line and determining preferentially treated obstacles; and inserting an optimal line intersection point around the obstacle, adjusting the line scheme and repeating the point insertion operation until the spatial relationship requirement of the line and the obstacle is met, and finally generating a global optimal line conforming to the constraint. According to the method, through a priority obstacle handling strategy and a dynamic point insertion mechanism, key obstacles are intelligently recognized, intersection points are preferentially generated around the obstacles, the problem of circuit bypassing redundancy or repeated adjustment caused by an improper obstacle handling sequence in a traditional method is avoided, and the efficiency and quality of circuit scheme design in a complex environment are remarkably improved. Through point-by-point insertion type iterative search, it is ensured that each adjustment approaches to the global optimum direction, and compared with a traditional trial and error method, the calculation time consumption is reduced, and meanwhile local optimum is avoided.

According to the real-time obstacle avoidance double-layer path planning method for the mobile robot in the nuclear environment, a grid map of the nuclear environment is prior information, each grid in the grid map comprises radiation field information and obstacle information, and a laser radar is carried on the mobile robot; the method comprises the following steps: S01, establishing a mathematical model for real-time obstacle avoidance path planning; s02, adopting an ACO-A *-PSO algorithm to plan a global path; and S03, an FL-DWA-VOA algorithm is adopted to plan a local path. The method is suitable for mobile robot planning in a multi-information-field coupling environment with unknown dynamic or static obstacles, firstly, an ACO-A *-PSO algorithm is adopted to plan a global path, inflection points on the global path are selected as sub-navigation points, then an FL-DWA-VOA algorithm is called to perform local path re-planning, a final path is formed, and the final path is obtained. And the final path also has the capability of avoiding unknown static or dynamic obstacles on the premise of ensuring global optimum.

The invention discloses a railway station energy consumption intelligent management method and system based on artificial intelligence. The method comprises the steps that sensors are deployed in subsystems of a railway station, and environment parameters, equipment operation parameters and historical energy consumption data are collected; a unified communication protocol and a standard interface are adopted, and any one or more subsystems of air conditioner ventilation, cold and heat sources, illumination and elevators and escalators are interconnected; according to the real-time environment change and the load demand, carrying out self-adaptive control on the plurality of pieces of energy consumption equipment; based on a mechanism model and an artificial intelligence prediction model, energy consumption simulation and comparison are carried out on different control strategies, and the global maximum is automatically searched through an intelligent algorithm on the premise of meeting passenger comfort and safe operation. According to the technical scheme, through deep combination of mechanism modeling and artificial intelligence, high-precision prediction and adaptive regulation and control can be realized in railway station multi-system energy consumption management, and the energy utilization efficiency is comprehensively improved.

The invention relates to the technical field of search path planning, in particular to a search path planning method and device based on deep reinforcement learning and an evaluation method. The search path planning method comprises the following steps: establishing an environment matrix about detection capability based on search environment parameters, and setting a starting point; the trained neural network outputs a Q value corresponding to each action based on the current position of the intelligent agent, a reward function and an environment matrix, and the reward function is designed according to the current position, the distance between the reward function and a target point, the detection capability of the neighborhood of the current position and the number of unvisited areas; and the intelligent agent executes each action according to the action corresponding to the maximum Q value. The problems that in the prior art, efficiency and stability are often difficult to consider at the same time when a reinforcement learning algorithm faces a complex task, the path planning time is possibly too long or the result deviates from global optimum, and the actual application effect is affected can be solved.

The invention discloses an intelligent wire harness section design method based on multi-data fusion, and belongs to the field of wire harness section design, and the design method comprises the following specific steps: I, collecting and preprocessing various data of wire harness design; iI, associating information in different data to construct a wire harness design map; iII, analyzing information in the atlas and extracting various characteristic data of the performance of the wire harness; according to the method, local optimum can be effectively avoided, a globally optimal solution can be effectively found, wire harness section design can explore wider design space, optimization pertinence is improved, the two methods can complement each other, the convergence speed is increased, the number of iterations needed by optimization is reduced, comprehensive performance evaluation can be provided, various environment variables can be considered, and the method is suitable for popularization and application. Therefore, the performance of the wire harness in practical application is better reflected, the uncertainty in the design process can be effectively dealt with, more reliable performance estimation is provided for design, and the optimization process of the design scheme is more efficient and accurate.

The invention discloses an unmanned aerial vehicle urban inspection task allocation method based on a two-stage heuristic algorithm, and the method comprises the steps: regarding an unmanned aerial vehicle urban inspection task as a multi-dimensional constrained dynamic path planning problem, and simplifying the dynamic path planning problem into a two-stage static planning problem for solving; the first stage is called heuristic feasible solution set search, and after a round of decision is executed and state transition is completed, a feasible solution set of a next round of decision is heuristically searched in multi-dimensional constraints; the second stage is heuristic decision path planning, and an optimal decision path is searched in a feasible solution set of multiple rounds of decision. According to the method, through two-order decomposition, a complex dynamic programming problem is decomposed into two layers of static linear programming problems, and heuristic algorithms are used in the two layers; on one hand, the difficulty of solving the global optimum problem is reduced, and on the other hand, the poor quality of the overall solution caused by the relative optimum of each round of decision solution is avoided.

The invention discloses a knowledge graph-driven line storage space joint optimization scheduling model construction method. The method comprises the following steps: determining an optimal scheduling target and an overall target function of row storage space joint flood control scheduling, and setting constraint conditions; under a knowledge graph theoretical framework, constructing a response relationship between single-class line storage space scheduling and a river flood discharge state; selecting an excess peak water level exceeding a river embankment control water level as a coordination variable, decomposing the overall objective function into system association constraint equations of a river embankment, a reservoir, a flood storage and detention area and a flood drainage pump station, and reducing a multi-class line storage space joint scheduling problem into a problem of optimization scheduling of each single-class line storage space scheduling; and respectively solving by adopting an improved particle swarm algorithm to obtain an optimal scheduling process of each single-class line storage space. The method not only solves the problem that global optimum is difficult to ensure by directly applying a traditional optimization algorithm due to many row storage space optimization targets and a competitive relationship, but also reduces the calculation time of the model.

The invention belongs to the technical field of heat dissipation of electronic equipment, and particularly relates to an embedded manifold microchannel and a global optimization method thereof. The invention provides a global optimization method of an embedded manifold micro-channel, and provides a micro-channel heat sink combining an embedded and manifold structure and a multi-objective optimization design method of the micro-channel heat sink in order to solve the problems that the heat dissipation heat flux density of a data center is gradually increased, and the power consumption index of a matched heat dissipation system is increased along with the heat dissipation heat flux density. A Pareto optimal solution set representing multi-target global optimum is solved by taking structure parameters and heat transfer flowing property parameters as optimization parameters and target parameters and combining means such as simulation calculation, parameter sensitivity analysis, target equation response surface method fitting and an NSGA-II optimization algorithm, so that the heat sink gives consideration to heat transfer and flowing properties, and the heat transfer and flowing properties of the heat sink are improved. And the power consumption of the heat dissipation system is the lowest on the premise of meeting the heat dissipation requirement of the data center.

The invention provides a horizontal subdivision productivity optimization method based on adaptive entropy deep reinforcement learning, and belongs to the field of oil and gas field development engineering, and the method comprises the following steps: constructing an adaptive entropy deep reinforcement learning strategy model, interacting with a horizontal well subdivision oil production reservoir shaft numerical simulator, storing horizontal well subdivision experience, and optimizing the horizontal well subdivision productivity. Training and updating the adaptive entropy deep reinforcement learning strategy model by using historical subdivision experience; the strategy model is applied, the oil reservoir state is input, and a subdivision optimization scheme at the current moment is rapidly output. Compared with an existing horizontal well subdivision oil extraction optimization method, the method has the advantages that a sequence subdivision optimization scheme can be quickly output, strategy coefficients are automatically adjusted, and the capacity of strategy exploration for global optimum is effectively improved. Meanwhile, the method is combined with an oil reservoir shaft coupling model, it is ensured that an optimization strategy is tightly attached to the actual physical process, the accuracy of yield evaluation is improved, and the purpose of rapid optimization of the horizontal well sequence subdivision scheme is achieved.

This invention discloses an IGBT lifetime prediction method based on a GA-Elman-LSTM combined model. Specifically, the method involves: acquiring IGBT aging data; preprocessing the data; using a GA-optimized Elman neural network to import the training dataset into the GA-Elman model for training; constructing error correction training and validation sets using the trained GA-Elman preliminary prediction model; training the LSTM error correction prediction model; and building a GA-Elman-LSTM error correction combined model based on the GA-Elman preliminary prediction model and the LSTM error correction prediction model for final combined prediction. This invention conveniently and efficiently enables the model to converge to the global optimum, effectively improving the model's prediction accuracy; it also improves the model's prediction stability and reduces prediction risk.

The invention discloses a high-voltage circuit breaker mechanical fault diagnosis method and system based on an MIDBO-SVM model, and the method comprises the steps: firstly obtaining a vibration signal of a circuit breaker, and then carrying out the diagnosis of a mechanical fault of the circuit breaker through the obtained vibration signal in combination with a pre-constructed MIDBO-SVM model. In the construction process of the MIDBO-SVM model, a good point set strategy and circle chaotic mapping are used for jointly generating an initial population to improve the uniform ergodicity of the population; a sine and cosine search strategy and a self-adaptive weight factor are introduced in a roll dung beetle individual position updating stage, so that decay oscillation is solved until the solution tends to a global optimal solution; the Cauchy Gaussian mutation strategy is adopted to mutate the individual with the best fitness at present in the mode of being far away from the optimal solution, the space search range is expanded, the problems that a traditional algorithm is low in later search speed and prone to falling into local optimum are further solved, and the fault classification precision of the circuit breaker is improved.

The invention discloses a multi-unmanned-aerial-vehicle cooperative path planning method based on an improved MPGWO algorithm, and belongs to the technical field of path planning, and the method comprises the steps: initializing a multi-unmanned-aerial-vehicle path planning task which comprises the target function of path planning, the position of an unmanned aerial vehicle, parameters and other information; then, constructing a three-dimensional environment map which comprises complex terrains and static cylindrical obstacles; secondly, relevant algorithm parameters are initialized, a population is initialized based on an SPSO algorithm, and a high-quality initial solution is generated; layering according to the fitness values, dynamically adjusting the number and the distribution mode of the sub-populations, and evaluating the fitness values of the individuals; secondly, independently optimizing each sub-population by adopting an improved MPGWO algorithm, and updating all individual positions meeting constraint conditions; the sub-populations share the global optimum alpha wolf every certain iteration times, and the fitness value and the global optimum solution of each sub-population are updated; and when the algorithm reaches the maximum number of iterations, outputting an optimal path. According to the method, by improving the MPGWO algorithm, the optimization efficiency and the solution quality of multi-unmanned aerial vehicle cooperative path planning are improved, the feasibility of the path is ensured, and finally efficient path planning and obstacle avoidance are realized.

The invention provides a wheeled robot navigation planning method in a non-flat environment, which combines local optimum and global optimum, disassembles a long-distance trajectory planning problem in a complex environment, firstly solves a global distance optimum trajectory to provide constraints, then locally solves a navigation problem, approaches the global optimum by using the local optimum, and finally solves the navigation problem by using the global optimum. And the solving efficiency is greatly improved. In the optimization process, the algorithm uses the geometrical characteristics of the ground surface to carry out dimension reduction on the loss function of the track, the landing characteristics of the track are considered, the method can well adapt to various complex and extreme ground surface topological structures, and the real-time performance and the landing constraint of the wheeled robot are considered while it is guaranteed that the track is approximately optimal.

The invention belongs to the technical field of semi-active suspension control methods, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm, which comprises the following steps of: performing adaptive grid division on an external file of the MOPSO algorithm to determine global optimum with the most diversity and global optimum with the most convergence; judging the evolution state of the population according to the distribution entropy variation of all optimal solutions in an external file, and selecting a global optimal gBest for guiding evolution; according to the variable quantity of the local crowding distance of the current population evolution, adjusting flight parameters of population evolution to balance the evolution trend of the population; and fusing the two core mechanisms, providing a multi-information fusion multi-target particle swarm optimization algorithm, and applying the multi-information fusion multi-target particle swarm optimization algorithm to determination of a semi-active suspension LQR control strategy weight coefficient. According to the novel multi-objective optimization algorithm, external archive information and current population dynamic information are cooperatively integrated, so that balance between convergence precision and population diversity is effectively realized.

The invention belongs to the technical field of unmanned system intelligent control and path planning, and discloses a multi-unmanned aerial vehicle path planning method based on a multi-population grey wolf optimization algorithm, and the specific technical scheme comprises the steps: 1, building an environment model and constraint conditions of unmanned aerial vehicle three-dimensional path planning; 2, constructing a multi-target cost function including fuel consumption cost, flight height cost, threat area cost, time cooperation cost and space cooperation cost; 3, optimizing the multi-target cost function by adopting a multi-population grey wolf optimization algorithm to obtain an optimal flight path; according to the method, the global search capability is remarkably enhanced, and the multi-group clustering strategy and the periodic convergence factor act together, so that the algorithm can effectively jump out of local optimum, a path scheme closer to global optimum is found in a complex solution space, the convergence speed and the calculation precision are greatly improved, the collaborative planning effect is excellent, and the method is suitable for large-scale popularization and application. The method shows strong environmental adaptability and robustness, and has a very high practical application value.

The invention discloses a presence / absence perception network structure optimization method based on an improved snake vulture algorithm, and the method comprises the steps: building a multi-target optimization model according to a heterogeneous presence / absence collaborative perception network structure based on the degree of nodes and the average value of the reciprocal of the shortest path between node pairs, initializing the parameters of the improved snake vulture algorithm, and carrying out the optimization of the multi-target optimization model. An upper triangular coding method is adopted to form individuals in the population, and Tent chaotic mapping is introduced. Solving the multi-objective optimization model by using an improved snake vulture algorithm, and updating the position and fitness value of the individual through two escape strategies of the snake vulture; gaussian and Cauchy variation disturbance is introduced to help the algorithm jump out of local optimum and search towards a globally optimal solution; a greedy strategy is introduced, and the algorithm is guided to keep tracking of an optimal solution in the search process by comparing fitness values. According to the invention, the optimal heterogeneous presence / absence cooperative sensing network structure under different tasks can be obtained, and the overall efficiency of the presence / absence system is improved.

The invention provides a fan heat dissipation adjusting method and control equipment, and relates to the technical field of power electronics. The fan heat dissipation adjusting method specifically comprises the steps that the initial layout of each power device and the size of a fan driving circuit are obtained; and constructing and determining an optimized layout based on initial local optimum through a position optimization model. Whether the heat dissipation characteristic and the convergence characteristic of the optimized layout meet preset conditions or not is sequentially judged, if neither the heat dissipation characteristic nor the convergence characteristic meets the preset conditions, and the heat dissipation temperature corresponding to the current initial local optimum meets a second preset threshold value, the current initial local optimum serves as a new initial global optimum layout, and the current initial local optimum serves as a second initial global optimum layout; returning to execute the step of determining the optimized layout based on the initial local optimum through the position optimization model until a second preset rule is met, and determining a final global optimum layout; and adjusting the layout position of the power device according to the final global optimal layout. According to the invention, the heat flux density among the components in the control device can be improved, so that the working performance and reliability of the control device are improved.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.

The invention discloses a passive time difference positioning method based on an improved Kepler optimization algorithm, and the method comprises the steps: firstly converting a passive time difference positioning equation solving problem into an optimization problem which takes the real position of a positioning target as a design variable, and designing a target function; secondly, using Logistic chaotic mapping to improve initialization of positions of candidate solutions, calculating fitness of the candidate solutions by using an objective function according to the positions of the candidate solutions, and taking the candidate solution with the optimal fitness as a current optimal solution; according to the positions of the candidate solution and the optimal solution, the gravitation between the candidate solution and the optimal solution is calculated, and the speed of the candidate solution is calculated; and finally, generating a random number, updating the position of a candidate solution, calculating fitness by using an objective function, updating global optimum, and finally outputting a position corresponding to an optimal solution to realize target positioning. The method is higher in global search and local search capability, higher in convergence speed, better in positioning effect and suitable for complex positioning scenes.

The invention discloses an artificial intelligence model hybrid quantification method and device based on multi-objective optimization, and relates to the technical field of artificial intelligence. The method comprises the following steps: randomly generating a super-average mixed quantized sample for a target model; parallel mixed quantization is carried out on the target model, and each mixed quantization sample and a mixed quantization result form a mixed quantization data set; using an evolutionary algorithm and a Bayesian neural network model to iteratively screen out a set of optimal mixed quantized samples from the mixed quantized data set; and screening out an optimal mixed quantized sample which enables the precision and the size of the target model to reach optimal balance, quantizing the target model to obtain an optimal quantized model, and finely adjusting the optimal quantized model by using the target domain data set to obtain a final quantized model. The method combines transfer learning, an evolutionary algorithm and a Bayesian neural network-assisted multi-objective optimization algorithm to improve the model precision and consider the model size at the same time, thereby realizing multi-objective global optimization.

The invention discloses an oil well working condition fault early warning method and system, and relates to the technical field of oil well control. According to the technical key points, the method comprises the following steps: firstly, extracting a 10-dimensional feature vector of oil well production data, and identifying an oil well which may have a fault through a fault early warning model based on improved fuzzy C-means clustering by using the 10-dimensional feature vector, according to the improved fuzzy C-means clustering algorithm, by purposefully selecting an initial clustering center and improving a target function, the defect that an FCM clustering algorithm is prone to falling into local optimum is overcome, and meanwhile the separation between the clustering centers is ensured; furthermore, a global search method-firefly algorithm is added on the basis, a calculation method of the clustering center is optimized, and a global optimal solution can be effectively found. The oil well fault can be timely and accurately found and treated in time, manual inspection is not needed, the working efficiency is improved, and management and supervision are facilitated.

The invention relates to an engineering optimization method and system based on a whale optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S1, initializing parameters and populations: setting a population scale, a maximum number of iterations, dimensions and upper and lower bounds of variables, generating an initial population, and initializing a global optimal solution and a current optimal solution; s2, executing an individual variation strategy: recording a global optimal individual position and a current iteration optimal individual position; s3, executing a unified search strategy; s4, executing a group communication strategy; and S5, judging whether the current number of iterations reaches the maximum number of iterations, if so, outputting a globally optimal solution, otherwise, adding 1 to the current number of iterations and returning to the step S2 to continue iteration. The algorithm provided by the invention aims to improve the global search capability and convergence precision of the algorithm and maintain the time complexity equivalent to that of the original WOA at the same time.

The invention relates to the technical field of ultrasound, in particular to an ultrasonic phased array sparse arraying method. Comprising the following steps: firstly, constructing a planar rectangular ultrasonic array model and determining basic parameters; setting a fitness function by taking a minimum maximum sidelobe level (MSLL) as an optimization criterion; secondly, introducing a tent chaotic mapping-good point set mixing method to obtain a classical solution population; a quantum bit coding strategy is introduced in the coding stage, individuals in the population are expressed as quantum bits, a standard NOA strategy is executed on the classical solution population to update the population, then the update quantity is synchronized to the quantum bits, and finally a new generation of classical solution population is generated through quantum rotation and collapse operation and the fitness is evaluated; integrating a covariance matrix adaptive evolution strategy (CMA-ES) in the optimization iteration process, and dynamically adjusting the shape, direction and step length of search distribution; and finally updating the global optimum. According to the method, the array element distribution of the sparse array is optimized through the multi-strategy fusion ENOA algorithm, the peak side lobe level of the array can be remarkably reduced, the anti-interference capability and precision of target detection are improved, and meanwhile, the hardware cost of the array is reduced.

The invention discloses a non-linear constraint coupling driven implicit modeling optimization method and system and a memory. The method comprises the steps of firstly obtaining nonlinear constraint data of a target geological interface, then generating initial implicit function representation through a Hermite radial basis function based on an initial control point of the target geological interface, and then obtaining a nonlinear coupling driving optimization objective function about implicit function representation of the target geological interface according to the data type of nonlinear constraint, and optimizing through an implicit gradient flow algorithm to obtain a geological interface reconstruction model and a global optimization target geological interface model. The invention further provides a computer readable memory, the memory contains a computer program for realizing the implicit modeling optimization method for nonlinear constraint coupling driving, the computer program can be read and executed, and the computer program, a programmable logic controller and a processor form an optimization system. According to the system, nonlinear constraint data of a large number of geological interfaces are fully coupled, an implicit modeling framework is constrained, and therefore high-precision modeling of the three-dimensional geological interfaces is achieved.

The invention relates to the technical field of power system network topology optimization, in particular to an offshore wind plant current collection system topology optimization method and system oriented to global optimum, and the method comprises the steps: collecting coordinate position data of all wind driven generators; carrying out site selection on the offshore transformer substation through a fuzzy C-means algorithm; connecting all candidate cables based on the coordinate position data of the wind driven generator; a topological optimization problem of a current collection system of an offshore wind plant is formulated based on an MILP model of power network extension planning, an optimized objective function is established, constraint conditions are set, and the objective function aims at minimizing the total cost of current collection system planning of the offshore wind plant; and optimizing and solving the MILP model to obtain an optimal topology design scheme. According to the method, various physical and economic constraints in the offshore wind power plant current collection system are simulated based on the MILP optimization model, the optimal current collection system layout can be generated, and the power transmission loss can be accurately evaluated.