30results about How to "Improve search capabilities" patented technology

The invention discloses an unmanned aerial vehicle path planning method based on global search and meta-heuristic optimization, and the method comprises the steps: firstly building an unmanned aerial vehicle path planning model under complex constraints, determining a fitness function and a constraint condition thereof, setting algorithm initialization parameters, carrying out the chaotic mapping of an initialization population position, and carrying out the optimization of the algorithm; and performing loop iteration solution by using a meta-heuristic optimization algorithm, outputting a global optimal path, finally realizing the global optimal path on the unmanned aerial vehicle, and calculating a flight distance corresponding to the optimal path to complete unmanned aerial vehicle path planning. According to the method, a meta-heuristic algorithm, namely a crown porcupine algorithm, is combined with tabu search, the global search capability is enhanced, and meanwhile, population initialization is optimized in combination with chaotic mapping, so that the randomness and unpredictability are increased, the algorithm can converge more quickly, and the robustness of the algorithm is improved. The method has obvious advantages in solving the problem of path planning with complex environment and complex constraint, and solves the problem of path planning of the unmanned aerial vehicle in the three-dimensional complex mountain environment.

The invention discloses a regional school site selection optimization method and system based on an improved black plinuan search algorithm, and the method comprises the steps: building a regional school site selection optimization model through an XGBoost algorithm, and enabling the regional school site selection optimization model to achieve the optimization of a multi-target optimization function composed of school bearing pressure, student school accessibility and school service coverage rate as a target; according to the method, a regional school site selection optimization model is solved through an improved Black-South optimization algorithm, and the improved Black-South optimization algorithm is obtained through a Sine chaotic mapping initialization population, a golden sine search strategy and the search performance of a Gaussian disturbance improvement algorithm, so that the diversity of the population is increased, and the global search and local optimization capability of the algorithm is improved. According to the method, the regional school site selection optimization model can be established through the XGBoost algorithm, the optimization speed can be effectively improved, and the global search and local optimization capability of the algorithm can be effectively improved by improving the Black-Youlinuan optimization algorithm and increasing the diversity of populations.

The invention discloses a method for predicting the residual capacity of a vehicle-mounted lithium battery, and the method comprises the steps: achieving the collection of battery data through a battery management system, analyzing the correlation of the multi-dimensional features of the data through a cloud server according to the data of a plurality of electric vehicle battery samples through a Pearson correlation coefficient algorithm, and screening out the strong features which greatly affect the capacity of the battery; performing standardization processing on the data; constructing an LSTM neural network prediction model according to the dimension of the input data; setting parameters of a chaos particle swarm optimization algorithm according to the neural network structure, and applying the optimal parameters obtained by the chaos particle swarm optimization algorithm to the LSTM neural network; and finally completing training and testing of the neural network model to obtain the LSTM neural network model capable of being used for prediction. According to the invention, the Pearson's correlation coefficient algorithm is utilized to carry out battery capacity correlation characteristic analysis, strong characteristics having great influence on the battery capacity are found out, and the accuracy of the model is improved; and the LSTM neural network model optimized by the chaos particle swarm optimization is used for prediction, so that the prediction precision is improved.

The invention relates to the technical field of workshop production scheduling, in particular to a distributed hybrid flow workshop scheduling method oriented to rocket tank production, and aims to construct a distributed hybrid flow workshop scheduling model including an assembly process by taking the maximum completion time of a minimized product as an objective function. According to the method, a multi-layer coding genetic algorithm framework is designed, and an elite selection strategy and a neighborhood search operator are introduced into the framework. The elite selection strategy ensures that high-quality individuals are reserved in the iteration process, and the convergence speed of the algorithm is increased; a network diagram method is adopted to identify key paths, neighborhood search is carried out on key workpieces, the scheduling bottleneck is optimized, the quality of solutions is improved, and local optimum is avoided. The method not only can greatly improve the scheduling efficiency of the rocket tank, but also has good applicability and universality, and can be widely applied to other types of distributed hybrid flow shop scheduling environments.

The invention relates to the technical field of computer vision and image processing, in particular to a space target three-dimensional reconstruction method based on epipolar line mapping constraint dense search. The processing can reduce the dependence on transformation matrix projection parameters in the weak texture region matching process. A kernel line coordinate system is established, orientation processing is carried out on the relative direction of a matched feature image pair, a new inter-pixel mapping relation is obtained, the inter-pixel mapping relation is used as dense search constraint in a triangulation network, and dense search has a better search effect in a weak texture area. And integrating the kernel line mapping search result and the existing epipolar constraint relationship, and finally performing multi-view reconstruction to obtain a target physical model. According to the method, dense point searching between matched image pairs can be effectively carried out, pixel point level searching precision can be achieved, and three-dimensional reconstruction of a space target is achieved.

The invention discloses a multi-workshop job task cooperative scheduling method based on tabu search and an NSGA-II fusion algorithm. The method comprises the following steps: S1, obtaining basic information of a job task set, a machine set and a workshop set in a workshop; s2, establishing a multi-workshop operation task collaborative scheduling multi-objective optimization model; s3, configuring a tabu search and NSGA-II fusion algorithm used for solving the multi-workshop job task collaborative scheduling multi-objective optimization model; s4, establishing an analytic hierarchy process decision model; s5, outputting a plan scheduling scheme; and step S6, carrying out visual display on the job task collaborative scheduling scheme. According to the method, the workpiece completion efficiency is improved, and the total machining cost and the total machine energy consumption are reduced.

This invention relates to the field of battery state prediction technology and discloses a method for predicting the health status of UAV power batteries. The key technical points of this method are: data acquisition and quantum encoding; quantum feature extraction; construction of a quantum-heuristic deep learning model; model training and optimization; and health status prediction and assessment. Through quantum encoding, a quantum-heuristic model architecture, and a dynamic optimization mechanism, this method overcomes the limitations of traditional methods in modeling complex nonlinear relationships and local optimization, providing a high-precision and robust solution for the health management of UAV power batteries, and has significant engineering application value.

The present application belongs to the technical field of tracked vehicles, and particularly relates to a method for determining the pre-tensioning force of a high-speed tracked vehicle, comprising the following steps: establishing a topological graph of a high-speed tracked vehicle system; establishing a discretized mass model of a lower track section; establishing a continuum verification model of the lower track section; checking the discretized mass model; establishing a dynamic model of the road wheels and the vehicle body; establishing a multi-level coupled vibration model of the vehicle; establishing an excitation model of road unevenness and different vehicle speeds; constructing an objective function of simultaneous passability and task load stability; setting the pre-tensioning force as a design variable and defining its variation range; using an optimization algorithm to optimize the pre-tensioning force under different structural masses and excitation conditions; and substituting the optimal solution of the pre-tensioning force into the multi-level coupled vibration model for solving and verification. The present application can provide theoretical support for setting the pre-tensioning force of a high-speed tracked vehicle.

The application provides a file retrieval method and system based on HBase, and relates to the field of computer systems.In the application, the field library defined by each business system in the incremental data is uploaded by each project group when uploading the file to the bank storage system through busid, and the field library extracted from the unstructured text by using an algorithm is trained by using a bert model or a chatGLM model in a natural language processing technology.The application is based on the technical scheme of hbase+es, the search feature of es is used to quickly and accurately search the unstructured data stored in the hadoop cluster, the unstructured data files meeting the conditions are searched in the storage system through the self-definable conditions, and the search capability of the unstructured data files of the storage system is enhanced.

The invention relates to a method and system for identifying a fire combustion form of oil-filled equipment of a transformer substation, and the method comprises the steps: carrying out the binarization processing of an original image, and segmenting a combustion region; calculating the area, height, width, centroid, number of connected regions and other parameters of the segmented region image; and inputting the parameters into a pre-established and trained improved particle swarm algorithm-support vector machine model, and finally obtaining a classification identification result of the fire combustion form of the oil-filled equipment of the transformer substation. According to the improved particle swarm algorithm-support vector machine model, an evolution rule of a genetic algorithm is added in a traditional particle swarm algorithm, the ability of the particle swarm algorithm in the aspect of optimization is optimized, the probability of falling into a local optimal solution is low, iterative search optimization is conducted on support vector machine kernel parameters, and the optimal kernel parameters are obtained. The method can identify the combustion form in time at the early stage of fire combustion of the oil-filled equipment of the transformer substation, and assists fire-fighting departments and intelligent fire-fighting equipment to carry out targeted fire extinguishing operation.

The application discloses a method for cooperative jamming based on intelligent optimization algorithm, comprising: constructing a jamming decision model, the jamming decision model comprising a cooperative jamming decision matrix, a gain matrix, a jamming matrix, a jamming gain matrix, a jamming bandwidth ratio factor, a jam-to-signal ratio, and a jamming benefit; establishing an objective function and a constraint condition of the jamming decision model according to the jamming benefit; and using an artificial bee colony algorithm to take the jamming benefit as a fitness function and optimize the cooperative jamming decision matrix A. In different complex electromagnetic spectrum environments such as limited spectrum resources and the same frequency band shared by jamming devices and illegal users, the limited jamming resources are reasonably distributed under the condition that the jamming device of the own side can normally communicate, so that greater jamming benefit is achieved; the algorithm convergence speed and search ability are improved, and the method is helpful for making a decision with higher jamming benefit in a shorter time.

The application discloses a mask image recognition method based on an evolutionary support vector machine. The application utilizes an improved differential evolution algorithm to evolve training parameters of a support vector machine based on a polynomial kernel function, and then utilizes the evolved support vector machine based on the polynomial kernel function to recognize whether a face image is a mask image. In the improved differential evolution algorithm, selection probabilities of mutation strategies are calculated first, then a population is divided into a guide subpopulation and a repulsion subpopulation, and meanwhile, directional information of guide individuals and repulsion individuals is utilized to improve search performance of the algorithm, so as to improve recognition precision of the mask image.

The invention provides a quantum computing auxiliary analysis and marketing prediction method for private domain traffic pool data, which comprises the following steps of: acquiring and processing user behavior data, coding the user behavior data into a quantum state by using a quantum algorithm, extracting user behavior characteristics by using a quantum support vector machine (QSVM), and analyzing a periodic mode through quantum Fourier transform, so as to realize the quantum computing auxiliary analysis and marketing prediction of the private domain traffic pool data. According to the method, a marketing prediction model is constructed based on a quantum algorithm, model parameters are optimized by using a quantum annealing technology, the prediction accuracy is improved, and the method further comprises the step of carrying out classical verification and optimization on a quantum calculation result so as to realize personalized marketing. According to the invention, efficient analysis and precise marketing prediction of the private domain traffic pool are realized, and the pertinence and effectiveness of enterprise marketing strategies are improved.

The application discloses an internet intrusion detection method and device based on an optimized related vector machine, and the method comprises the following steps: acquiring an NSL-KDD data set, including a training set and a test set; performing non-numerical one-hot encoding and numerical normalization processing on the NSL-KDD data set to obtain the preprocessed training set and test set; inputting the preprocessed training set into a related vector machine model which is pre-constructed and optimized based on an improved crow search algorithm to perform training, and obtaining the optimized and trained related vector machine model; inputting the preprocessed test set into the optimized and trained related vector machine model to perform testing, and obtaining a classification detection result. The application has better convergence, and can improve the classification accuracy of the intrusion detection data set and reduce the false positive rate.

The application relates to a heterogeneous multi-sensor joint deployment method for low-altitude target detection, comprising the following steps: constructing three-dimensional monitoring grid units covering the ground and low-altitude airspace of a region to be monitored, and determining a multi-sensor set to be deployed and continuous state variables and discrete state variables thereof; judging the line-of-sight unobstruction of each three-dimensional monitoring grid unit by using a digital elevation model, and calculating the comprehensive detection probability of each three-dimensional monitoring grid unit for cooperative targets and non-cooperative targets under multi-sensor cooperative detection according to the result of the line-of-sight unobstruction judgment; constructing a multi-objective optimization function with the minimum total cost and the maximum monitoring coverage rate as the targets, wherein the monitoring coverage rate is determined based on the comprehensive detection probability; and solving the multi-objective optimization function by using a hybrid evolutionary algorithm to output a multi-sensor deployment scheme. The scheme can improve the global search capability and convergence efficiency of the algorithm, and obtain a sensor station deployment scheme considering the network construction cost and the three-dimensional coverage rate.

The present invention relates to the field of data processing, and more specifically, to a method and system for setting relay protection for a distribution network based on multi-source data. The method comprises: in a gray wolf optimization algorithm, determining the contribution weight of each relay, obtaining the fitness of the relay according to an objective function, selecting the positions of three optimal relays from a population, adjusting the positions of the remaining relays based on a coordination vector, and correcting the convergence factor to obtain a dynamic step factor. For any target relay, the slope between it and the adjacent relay is calculated to obtain an update coefficient. The dynamic step factor is multiplied by the update coefficient to obtain an adjusted search amount to guide the update of the relay position. In a preset number of consecutive iterations, the number and mean of non-zero update coefficients are recorded, and a convergence value is calculated. Whether to terminate the iteration is determined based on the convergence value, and the position and optimal fitness of the optimal relay are output to improve the search capability and convergence speed of the algorithm.

This invention discloses a parameter estimation method, system, device, and storage medium for a nonlinear diffusion model. The parameter estimation method includes: constructing a spatial basis function based on system data snapshots for a nonlinear Fisher-type diffusion system applied to the heating and cooling process of an iron rod, and separating spatiotemporal variables using an orthogonal decomposition method; sparsely sampling the nonlinear terms using a discrete empirical interpolation method to obtain the optimal low-order approximation of the high-order system; initializing a particle swarm to obtain n particles corresponding to the m-dimensional solution vector of the low-order time series model, and calculating the fitness of each particle according to the calculation formula of the parameters to be identified and the objective function; iterating and optimizing each particle, and outputting the position of the particle with the global optimum value; adding a non-Gaussian Levy process to the traditional particle swarm algorithm, and avoiding premature concentration of the particle swarm in the same direction through the random jump of the Levy process, thereby increasing the mutual learning ability between particles.

This invention discloses a path optimization method based on cooperative aggregation and branch deviation, comprising the following steps: (1) Initialize the population and variables, and divide the population into two subpopulations s1 and s2; (2) Calculate the fitness of individuals in subpopulations s1 and s2 according to the fitness function; sort the fitness of individuals in the two subpopulations from smallest to largest, and select the optimal individual; (3) Keep the i1 and i2 individuals with the highest fitness in subpopulations s1 and s2 respectively, and take the remaining individuals as clustered individuals; (4) Apply the cooperative aggregation strategy to the clustered individuals; (5) Take the top m1% and m2% of s1 and s2 respectively according to fitness to synthesize a new population s3; (6) Update the individuals in s3 randomly using the replacement strategy, and update the global optimal individual; (7) Determine whether the termination condition has been met. If not, increase the population diversity using the crossover operator and mutation operator; (8) Iterate according to steps (2)-(7) until stopping, and output the optimal path.

This invention provides a reinforcement learning-based method for finding the nearest point and navigating a patrol robot, comprising: Step 1: Setting two routes A and B that the patrol robot needs to inspect; Step 2: Obtaining the initial position coordinates of the current patrol robot and activating the LiDAR; Step 3: Obtaining the current yaw angle of the patrol robot, allowing the robot to move straight for m seconds, and obtaining the current position coordinates of the patrol robot, unifying the coordinate system; Step 4: Selecting the nearest path point as the initial point for tracking; Step 5: Connecting the initial point with the next path point to obtain the angle relative to the IMU coordinate system, controlling the point-to-point movement of the patrol robot; Step 6: When patrolling route A is completed, starting patrolling route B, using a reinforcement learning algorithm to select the nearest path point to route B as the initial point for patrolling. This invention's method has high efficiency and accuracy, significantly reducing the computational complexity of existing technologies.

The application discloses a neural network distributed automatic parallel training method based on AC reinforcement learning. Firstly, the application performs performance data sampling analysis on a neural network model based on a performance analysis method, extracts performance data of model operators, and realizes operator grouping fusion. Secondly, global feature vector representation of a calculation graph is realized, and a state search space of reinforcement learning is constructed. Then, an AC algorithm based on time series difference sampling is adopted to complete iterative optimization of the reinforcement learning model, so as to search for an optimal distributed parallel strategy. Finally, a multi-thread simulation executor based on event driving is constructed to simulate the calculation process of the neural network model. The application reduces the state search space scale of the AC reinforcement learning, improves the search performance and universality of the parallel strategy, reduces the strategy iteration execution time, and reduces the hardware execution cost in the iterative search process.

The invention provides a flexible job shop machine and AGV combined scheduling method, system and device, and the method comprises the steps: carrying out the training of a Q-FOX through a certain number of iterations, obtaining an optimal hyper-parameter, and fixing the value of the hyper-parameter; solving the joint scheduling problem of the flexible job shop machine and the AGV through the value of the hyper-parameter; and constructing a decision process model capable of dynamically adjusting parameters, and searching an optimal solution with an optimal adaptive value from the whole population based on a population fitness function so as to automatically adjust the joint scheduling parameters of the flexible job shop machine and the AGV. According to the method disclosed by the invention, by utilizing the hyper-parameter automatic tuning function of the Q-FOX algorithm, the convergence speed of the algorithm can be improved, the solving time is shortened, and the robustness of the reinforcement learning algorithm is improved.

The application discloses a reinforcement learning driven heuristic solving method for multi-ship co-dock maintenance scheduling, and three global search operators are designed for the multi-ship co-dock maintenance scheduling problem. One is a ship crossover operator, which is used to adjust and exchange the maintenance order of the ship. The second is a maintenance task destruction operator, which breaks the original task structure by extracting part of the task from the current maintenance task sequence, providing a larger optimization space for subsequent search. The third is a maintenance task recombination operator, which inserts the extracted maintenance task into the existing maintenance task list to form a new maintenance task order, thereby improving the possibility of obtaining a better scheduling scheme. The execution process of the global search operator is adaptively guided, and a more suitable search operation is dynamically selected according to the state information in the solving process, so as to reduce the invalid search overhead in the meta-heuristic algorithm, improve the utilization efficiency of the computing resources, and further improve the solving efficiency and solving effect of the multi-ship co-dock maintenance scheduling problem.

The application discloses a photovoltaic data management method based on a self-organizing network and related devices, and relates to the technical field of data processing. The method comprises the following steps: establishing a connection between each wireless communication node based on the performance interaction of a communication link and the connectivity between adjacent wireless communication nodes to form a self-organizing network; determining a target transmission path based on the time delay overhead and the signal-to-noise ratio of each communication link by using a hybrid algorithm to transmit photovoltaic data; classifying based on the feature matrix of the photovoltaic data to obtain photovoltaic data of each category; performing security requirement analysis on the photovoltaic data of each category to obtain a target requirement security level; performing service requirement degree analysis on the photovoltaic data of each category to evaluate the cold and hot degree thereof; and determining a storage strategy based on the cold and hot degree and the requirement security level of the photovoltaic data of each category in combination with storage node performance analysis to store the data into corresponding storage nodes. The application can achieve a more ideal effect for the storage management of photovoltaic data.

The application discloses a kind of based on simulated annealing algorithm's repair tree construction method, including obtaining node number and corresponding adjacency matrix;Set initial parameter and control parameter;Randomly generate initial Prufer sequence and execute simulated annealing algorithm, record the maximum bottleneck bandwidth corresponding Prufer sequence as current solution;Current solution produces disturbance and obtains legal new solution;Calculate the bottleneck bandwidth of new solution and current solution and decide whether to accept new solution;Repeat the above two steps until the set condition is met, obtain the Prufer sequence corresponding to the maximum bottleneck bandwidth and decode to get rootless tree, the auxiliary joint in rootless tree is used as root node and the final repair tree is obtained, and the application also discloses a kind of data recovery method comprising the repair tree construction method based on simulated annealing algorithm of the application.The encoding and decoding speed of the application is fast, algorithm is flexible, search effect is good and reliability is high.

The application provides an information search method, device, equipment, storage medium and program product. The method comprises the following steps: obtaining target search information input by an object, performing data recall based on the target search information, obtaining a plurality of candidate recall data, and screening effective behavior data related to the target search information from a plurality of historical interaction behavior data of the object, so as to determine the display result of each candidate recall data based on the effective behavior data, and finally display the candidate recall data based on the display result. By screening the historical interaction behavior data related to the target search information, useless information can be effectively eliminated, the interference of irrelevant data on the search result is reduced, the accuracy of the search system in predicting user preferences and search requirements is improved, the display result of each candidate recall data can accurately match the search requirement of the user this time, and the search effect is improved.

The invention belongs to the technical field of quadrotor unmanned aerial vehicles, and particularly relates to a quadrotor unmanned aerial vehicle mapping and planning method based on deep reinforcement learning, and the method comprises the following steps: S1, feature extraction and screening, S2, feature map creation, S3, feature map position calculation based on value iteration, and iteration through geometric verification and using a TD algorithm, and S4, state estimation. A planning method of quadrotor unmanned aerial vehicle mapping based on deep reinforcement learning is used for planning a generated flight trajectory of quadrotor unmanned aerial vehicle mapping, and the planning method comprises the following steps: S5, generating a strategy network model of the trajectory; according to the method, the common feature points and the feature point set between the frames can be rapidly extracted, the correlation graph of the feature points and the feature set is determined, and finally the corresponding relation and the position of the graph are calculated through a reinforcement learning method to obtain the accurate feature point relation.

The embodiment of the invention discloses a target object searching method and device, electronic equipment and a storage medium. The target object searching method comprises the steps that feature data of a to-be-searched target object is obtained, and a reference monitoring camera corresponding to the to-be-searched target object is captured for the first time; determining a candidate monitoring camera group based on the monitoring range of the reference monitoring camera, the trajectory features of the target to be searched and the spatial-temporal distribution relationship of the monitoring cameras; performing hierarchical feature matching on the image data acquired by the candidate monitoring camera group; and according to the matching result and the coverage range of the candidate monitoring camera group, generating a search result corresponding to the to-be-searched target object, so that the target object search efficiency and precision can be improved, and the search effect is improved.

The invention belongs to the technical field of workshop production scheduling, and discloses a distributed hybrid flow workshop scheduling method and system considering worker fatigue, and the method comprises the steps: defining a problem, and constructing a target function and a constraint condition; the workpieces are distributed to each factory, each machine and each worker in a balanced mode, and a workpiece scheduling sequence, a machine distribution vector and a worker distribution vector are obtained; performing crossover operation on the workpiece scheduling sequence to obtain a first workpiece scheduling sequence, and performing mutation operation on the machine distribution vector and the worker distribution vector to obtain a first machine distribution vector and a first worker distribution vector; and operating the first workpiece scheduling sequence of the key factory to obtain a second workpiece scheduling sequence, calculating the value of the target function based on the second workpiece scheduling sequence, and outputting a final workpiece scheduling sequence. According to the method, the constraint of worker fatigue is considered to establish the mathematical model, the multi-objective evolutionary algorithm based on Q learning is adopted for solving, and the maximum completion time and the total energy consumption can be minimized at the same time.

This application provides a key gene network search method and system based on intelligent agents, belonging to the field of gene search technology. The method includes: acquiring multiple search data sets for searching key genes of target genes; for each search data set, scoring each gene in the whole genome based on the search data; determining a candidate gene set corresponding to the search data set from the whole genome based on the scoring results; performing set operations on the candidate gene sets corresponding to each search data set to obtain a target candidate gene set; for each target candidate gene in the target candidate gene set, treating the target candidate gene as a network node, and determining the node attributes of the network node based on the score of the target candidate gene in each candidate gene set; generating edges between each network node based on preset gene association evidence and each target candidate gene to obtain a key gene network. This application can improve the accuracy of key gene search.

The application belongs to the technical field of power systems and discloses a power distribution network distributed resource optimization aggregation method based on an ant colony simulated annealing algorithm; the method first acquires parameters of distributed energy in a virtual power plant, constructs an objective function with the aim of minimizing the total cost of the virtual power plant, sets a constraint condition, and finally solves the objective function by combining an ant colony algorithm and a simulated annealing algorithm to output an optimal solution, namely, an optimal aggregation method of the distributed resources of the power distribution network; the application can jump out of the trap of a local optimal solution, enhance the global search capability, improve the convergence speed, and significantly improve the probability of finding a global optimal solution; the cooling strategy of the simulated annealing algorithm accelerates the late convergence, the pheromone mechanism of the ant colony algorithm strengthens high-quality solutions, and the cooperation of the two can improve the economy and stability of the scheme; by setting the constraint, the aggregation scheme can meet various operation requirements, reduce the scheduling cost, improve the resource utilization rate, realize the efficient utilization and optimal allocation of resources, and the like.