104 results about "Global optimum" patented technology

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 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 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.

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 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 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 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 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 multi-spectral radiation temperature measurement method and system for improving a quantum particle swarm, relates to the field of radiation temperature measurement and photoelectric signal processing, and solves the problems that when an existing standard PSO algorithm is applied to high-dimensional and nonlinear multi-spectral inversion, local extreme values are likely to be trapped, and the self-adaptive capacity is poor. Setting parameters, generating an initial particle position by using Tent chaotic mapping, calculating the particle fitness of an initialized population, updating initial individual optimum and global optimum, and calculating an average optimal particle position; updating the optimal particle position through a Levy flight mechanism; and repeating the steps, calculating the multispectral radiation temperature measurement target function value of the updated position, updating the initial individual optimum and the global optimum, and if an error threshold value or the maximum number of iterations is met, outputting the global optimum solution. The method is also suitable for the application field of inverting the real temperature and the spectral emissivity of the target from the multi-channel spectral radiation signals and the like.

The invention provides a coal machine equipment production plan optimization method based on a variable neighborhood search algorithm, and relates to the technical field of engineering scheduling. The method comprises the following steps: 1, initializing algorithm parameters; 2, constructing a priority ranking set and a total cost function; 3, setting and optimizing an initial solution; 4, generating a neighborhood solution set and updating the neighborhood solution set to obtain a candidate solution set; 5, selecting a neighborhood structure; 6, searching a neighborhood to obtain a local optimal solution; 7, updating the optimal solution; 8, optimizing the optimal solution; and 9, outputting a globally optimal solution after the execution of the number of iterations is completed. According to the method, the initial solution and the neighborhood structure of the variable neighborhood search algorithm are improved, the quality of the initial solution is optimized, premature falling into local optimum is avoided, the approximate optimal solution can be obtained for the coal machine equipment production plan optimization problem, more effective decision support is provided for decision makers, and therefore the efficiency and benefits of equipment batch production are improved.

The invention discloses a Czochralski crystal growth aperture contour extraction method, which comprises the steps of obtaining and preprocessing a crystal growth image, weighting and calculating a grey-scale map according to red, green and blue channels, carrying out normalization processing on a grey-scale value, calculating a gradient amplitude image by adopting an edge detection operator, generating an initial contour set, and extracting the aperture contour of the Czochralski crystal growth according to the initial contour set. Iterative optimization is carried out through a group optimization algorithm, a function containing constraint conditions such as a curve opening, an overlapping definition domain and average width and a double-target weighted sum of a gradient mean value and a regional gray mean value is used as an optimization function, and after 20-50 times of iteration, a global optimal aperture contour is output. According to the method, the robust and accurate extraction of the aperture contour is realized by introducing the optimization function of the multi-constraint and double-target weighted sum and combining the group optimization algorithm. According to the method, manual intervention is not needed, key data can be obtained in real time, support is provided for accurate regulation and control of Czochralski method crystal equal-diameter growth, and the yield and the automation level of crystal production are effectively improved.

The invention provides a wireless communication method, system and device based on fusion of a bidirectional intelligent metasurface and non-orthogonal multiple access of a movable element. The method comprises the following steps: receiving a superposed signal sent by a base station; based on a preset constraint condition, calculating the historical channel state information through a preset operation optimization algorithm to generate a driving instruction; according to the driving instruction, driving each movable element to move to a target position; electromagnetic regulation and control are carried out on the received superposed signal to generate an electromagnetic regulation and control signal, and the electromagnetic regulation and control signal is transmitted to the user terminal; the user terminal converts and decodes the electromagnetic regulation and control signal to generate a user data stream; and enabling the user terminal to estimate the current channel state based on the current user data flow, and generating and outputting updated channel state information. According to the method, the layout form of the metasurface can be dynamically optimized, and higher anti-interference capability and better multi-user fairness are realized while the performance is ensured to approach global optimum.

The invention discloses a laying hen lossless data processing method based on improved XGBoost, and relates to the technical field of data processing, discrete feature numeralization is realized through adaptive tag coding, abnormal value detection and non-linear mapping correction are combined, a robustness purification feature set is constructed, and a layer lossless data processing method based on the improved XGBoost is obtained. The problems that a traditional method is sensitive to sensor noisy points and lacks an effective data cleaning mechanism are solved, and prediction precision reduction caused by extreme sample interference is avoided. And performing nonlinear expansion on the purification features by using a polynomial kernel, and accurately capturing a coupling relationship between the features. The global optimal hyper-parameter optimization of the XGBoost integrated regression model is driven by high-dimensional features, so that the parameter optimization efficiency is improved, local optimum is avoided, the method adapts to a multi-source heterogeneous and high-noise nonlinear data scene of laying hen breeding, the processing precision and generalization ability of the model are remarkably improved, reliable data support is provided for accurate management and quality monitoring of laying hen breeding, and the method is suitable for large-scale popularization and application. And efficient landing of the intelligent breeding technology is promoted.

The invention discloses a monocular vector high-precision map data association method based on geometric context, and relates to the technical field of map data association. Extracting a map landmark set in a current frame view range from the map through the initial vehicle pose, and projecting a point set to a pixel plane; performing structured modeling on each point set in the detection road sign set and the projection road sign set; constructing a cost matrix for the current estimated vehicle pose; introducing a gating mechanism based on map distance and a self-adaptive gating range based on map depth, and eliminating matching pairs with inconsistent geometric structures; complete data association is obtained through cost matrix calculation, optimal bipartite graph matching is performed on the cost matrix by adopting a Hungary algorithm, and a data association relationship meeting global optimum is obtained. According to the method, feature information of road signs in a vector high-precision map is fully utilized, outlier matching relation pairs are eliminated through self-adaptive gating, finally, data association is conducted through a Hungary algorithm, and matching robustness and accuracy are improved.

This invention discloses a network path optimization method and system. The method includes: adjusting the adaptive weights of the heuristic function in the underlying model LPA* algorithm to intelligently change the magnitude of the heuristic function's influence with iteration; simultaneously, introducing a bias p in the K-value calculation to improve the algorithm's search efficiency; and performing unbalanced allocation of initial pheromones for the ant colony algorithm based on the initial path calculated by the underlying model. The upper-layer model applies the ACO algorithm to plan fiber optic network paths on a grid map to obtain the globally optimal network path. This invention, through the design of a hierarchical algorithm, provides the ant colony algorithm with prior information including pre-selected paths, enhancing the traditional ant colony algorithm's ability to guide pre-selected areas in path planning. This enables faster search for feasible paths, effectively solving the problems of slow search speed and susceptibility to local optima in the traditional ACO algorithm, and significantly improving the overall performance of the fiber optic network planning system.

The application discloses a kind of urban road network vehicle macroscopic path planning method for common congestion, belong to traffic path planning field;The method uses double-layer planning model, including macroscopic path planning and local path planning;In macroscopic path planning, the effective management of large-scale urban road network is realized by using existing traffic subarea division method and macroscopic traffic flow distribution strategy;Local path planning is then based on the traffic subarea passing order given by macroscopic planning, and within each traffic subarea, A* algorithm is used to plan a time-consuming shortest path for vehicles from the boundary of the previous subarea to the boundary of the next subarea. In order to deal with the common traffic congestion of urban road network, the application uses the flow balance theory to distribute traffic flow from the macroscopic level under the global perspective. According to the macroscopic path planning algorithm, the flow between the current traffic subareas is dispatched, so that the urban traffic flow reaches the global optimum.

The invention discloses a traffic control subarea optimization and self-adaptive adjustment method and system, relates to the technical field of subarea optimization, and is used for solving the problem that the coordination effect in the peak period of traffic flow is poor due to the lack of dynamic response to real-time flow data when an existing traffic signal coordination system carries out regulation and control based on experience or a fixed algorithm. The coordination effect is quantified by calculating the overlapping length and the directional delay, so that the optimization process is more objective, the coordination effect of different initial phase differences can be quantified, the optimal initial phase difference is selected, a group of coordination control schemes is generated by considering the coordination of non-adjacent intersections, the problem of global incoordination caused by local optimization is reduced, and the optimization efficiency is improved. According to the method, factors such as path length and traffic flow weight are introduced, coordination deviation between local optimum and global optimum is calculated, coordination loss is quantified more accurately, a smooth transition strategy is adopted, and sudden traffic fluctuation caused by overlarge phase difference change is avoided.

The application provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling.The method comprises the following steps: encoding and analyzing workpieces and machines based on machine indexes; in the case of parallel machine scheduling, initializing algorithm parameters based on the hybrid particle swarm algorithm, initializing a population by a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimization of the maximum completion time as an objective, executing a variable neighborhood descent local search strategy, and updating the local optimum and the global optimum; and in the case that an iteration index exceeds an iteration threshold, determining the end of the algorithm and outputting a target result to represent the allocation and scheduling information of the workpieces and machines.The application considers fuzzy processing time, learning effect and deterioration effect, and considers the scheduling problems of single machines and non-single machines in a fuzzy environment based on actual production conditions, which is helpful for iron and steel plants to formulate production strategies and reasonably arrange the allocation and scheduling of workpieces and machines.

The invention relates to the field of data processing, and provides a distribution comb contour determination method based on three-dimensional data acquisition, a medium and equipment. The method comprises the following steps: clustering an obtained contour curve of a specific area of the head of a user to generate a centroid-like curve, dividing the centroid-like curve into segmented centroid curves according to a preset interval, and screening segment curves with the similarity reaching the standard in a corresponding cluster as fitting candidate curves; and establishing a multi-objective optimization function containing curvature continuity, second-order derivability and anatomy adaptation degree, performing global optimal combination on the fitting candidate curves by using a dynamic programming algorithm to generate an initial contour point location sequence, and performing smooth optimization to form a contour curve of the distribution comb attached to the scalp side. According to the method, user head shape feature categories are divided through clustering analysis, an initial hash point set is generated by using a clustering center, the curve fitting degree is optimized in combination with a standardized smoothing algorithm, and finally a standard curvature template adaptive to a corresponding group is output.

This application provides a method, device, medium, and program product for determining the envelope region of reference points for parts. The method includes: obtaining the initial spatial coordinates of all reference points for a target type of parts; determining the first reference coordinates corresponding to all parts and the second reference coordinates corresponding to each part; then determining the offset of the initial spatial coordinates based on the first and second reference coordinates, and adjusting the initial spatial coordinates using the offset to obtain the corrected spatial coordinates of each part reference point; finally, combining the reference point types of all parts and the corrected spatial coordinates to determine the envelope region of all reference points for the target type of parts. This application's embodiment adjusts the initial spatial coordinates based on the offset of each part and calculates the envelope region of the part reference points based on the corrected spatial coordinates, achieving a part envelope region that approaches global optimum, significantly reducing the structural redundancy and development cost of part measurement supports.

The invention relates to a four-axis unmanned aerial vehicle PID control system parameter setting method based on a Harris eagle optimization algorithm, and belongs to the field of embedded systems. According to the method, the limitation that traditional PID parameter setting depends on artificial experience, efficiency is low and global optimum is difficult to approach is broken through, and autonomous and efficient optimization of the parameters of the PID controller of the unmanned aerial vehicle is achieved by introducing the Harris eagle optimization algorithm and utilizing the collaborative hunting strategy and the self-adaptive escape energy mechanism of the Harris eagle optimization algorithm. The method comprises the following steps: firstly, establishing a six-degree-of-freedom kinetic model of the four-axis unmanned aerial vehicle based on a Newton-Euler equation, taking parameters of an inner ring PID controller and an outer ring PID controller as optimization variables, and designing a multi-target fitness function fusing ITAE indexes, overshoot and adjustment time; then, executing intelligent search in a parameter space by utilizing a Harris eagle algorithm; dynamically switching global exploration and local development through escape energy, and gradually approaching an optimal parameter; and finally, the optimized parameters are deployed to a flight control system, and high-precision robust control over the attitude and the position in the complex disturbance environment is achieved.

The application discloses a blockchain-based economic dispatching method, comprising the following steps: obtaining a distributed power supply; constructing an Ethereum private blockchain network based on the distributed power supply; obtaining distributed power supply output power based on the Ethereum private blockchain network; performing economic dispatching calculation through a gradient correction algorithm based on the distributed power supply output power; and obtaining an economic dispatching result. Through the above technical solution, the application can make the economic dispatching reach the actual global optimum in real time and safely.

This invention discloses a method for identifying the vacuum level of a vacuum circuit breaker's arc-extinguishing chamber based on terahertz technology, specifically relating to the field of power equipment condition monitoring and fault diagnosis technology. The method involves establishing a vacuum detection platform for the arc-extinguishing chamber of a vacuum circuit breaker; using this platform to collect data samples of the vacuum level of the arc-extinguishing chamber of a vacuum circuit breaker at different pressure levels; performing noise reduction processing on the data samples and extracting feature values; establishing a vacuum level identification model for the arc-extinguishing chamber of a vacuum circuit breaker based on XGboost; using the Bald Eagle Search optimization algorithm for optimization training to obtain the global optimum; adjusting parameters based on the optimum value; determining the global optimum value based on the BMS-XGboost model as the initial parameters of the system; and obtaining the vacuum level classification model.

A method for designing the initial structure of a stationary zoom system includes the following steps: S1, establishing the basic structure of a classic stationary zoom system, determining the mathematical range of key parameters of the intermediate zoom group, and realizing the partitioning of the solution region and parameter dimensionality reduction; S2, introducing a rear focusing group, and introducing minimum braking constraints for deformable mirrors and realizing continuous zoom and basic aberration constraints based on free space; S3, establishing a nonlinear evaluation function, and obtaining the globally optimal numerical solution within the boundary conditions of the set variables and the analytical region of the structure. This invention solves the problem of not being able to obtain a convergent solution when directly using optical design software for optimization design. By incorporating the telescope structure into the stationary zoom system to reduce the dimensionality of key parameters, the numerical solution of the initial structure is calculated. This process involves a series of equations and constraints describing the stationary zoom system, which can effectively correct aberrations, achieve high zoom efficiency, and maintain a stable image plane at the required magnification.

The invention provides a rapid calculation method for limit clearing time based on an improved Bayesian optimization algorithm, and relates to the field of power systems, and the method comprises the steps: obtaining original parameters of different faults of a power system, and discretizing the original parameters into an integer optimization model; the method comprises the following steps: initializing an exploration domain, sampling to generate an initial data set, constructing a Gaussian process model based on the initial data set, calculating a corresponding acquisition function to obtain a next most potential sampling point, and updating the exploration domain; adding observation data corresponding to the new sampling points into the initial data set, updating a Gaussian process model according to the Bayesian theorem, and repeatedly calculating a corresponding acquisition function until a preset convergence criterion is met or the maximum number of iterations is reached, so as to obtain an optimal model; and calculating the lower limit clearing time of each fault of the power system based on the optimal model. According to the method, the continuous optimization problem is discretized, the exploration interval is dynamically cut in the iteration process, the evaluation number is reduced, and a solution close to the global optimum is quickly found.

The application discloses a multi-target distribution and path planning method based on a simulated annealing algorithm, and first, a multi-target distribution model is established, relevant variables are defined, and a target function is constructed to minimize total flight cost while considering task execution time and fuel limit; secondly, a double-loop model is constructed, the problem is abstracted into a Hamilton loop problem, including an outer Hamilton loop and an inner Hamilton loop, which are responsible for path planning between regional nodes and path planning of targets in a region respectively; finally, a simulated annealing algorithm is used to solve the TSP model of the double Hamilton loop, and through parameter setting, initial solution generation, solution transformation, Metropolis criterion application and cooling strategy, an optimal multi-target distribution strategy and corresponding path planning are found. The method can effectively handle large-scale, multi-target and multi-constraint complex problems, improve calculation efficiency, and find a solution close to the global optimum, and has important practical application value and market prospect.

This invention discloses a method for constructing QC-LDPC codes that minimizes the number of short loops, comprising the following steps: Step 1: Randomly generate the initial QC-LDPC code base matrix according to the construction requirements; Step 2: Count the number of short loops passing through each element in the base matrix; Step 3: Sort all elements in the base matrix according to the number of short loops; Step 4: Iterate through each element sequentially, calculate the number of short loops corresponding to all values ​​of the element, select the value with the fewest short loops as the new value of the element, if the element value changes, return to Step 2 for the next round of optimization, otherwise continue to traverse the next element; Step 5: Replace each element in the base matrix with a CPM (Current Minimum Quantity). This invention performs global optimization on all elements in the entire matrix, which can approach the global optimum solution, and does not rely on mathematical theories such as finite fields, has no restrictions on parameters, and has great flexibility.