6 results about "Greedy selection" patented technology

The application discloses a kind of solid crystal machine temperature sensitive point selection method, device, equipment and storage medium, belong to equipment manufacturing technical field.The method comprises: obtaining the temperature data of first preset quantity temperature measuring points of each position point of solid crystal machine;The position point of solid crystal machine includes second preset quantity position points;Using improved AP clustering algorithm, in combination with multiple preset cluster numbers, the temperature data of each position point obtained is clustered, and the clustering result corresponding to multiple preset cluster numbers of each position point is obtained;According to multidimensional index, the clustering result corresponding to multiple preset cluster numbers of each position point is analyzed, and target cluster number is determined;Based on the clustering result corresponding to multiple preset cluster numbers of each position point, in combination with target cluster number, determine candidate point set;Temperature sensitive point set is determined from candidate point set using greedy selection algorithm.The effectiveness and accuracy of temperature sensitive point selection can be improved using the technical scheme provided by the application.

The application discloses an open source dialogue model-oriented automatic jailbreaking prompt word generation and attack method and system. The application first screens a prompt word set from an original prompt word set; secondly, based on an attack question, the prompt word with the optimal attack efficiency is screened out from the original open source prompt word set, multi-path parallel testing is carried out by using a proxy model, and the attack success rate and the prompt word length of different prompt words in the prompt word set are evaluated in real time through a dynamic evaluation mechanism based on a greedy selection strategy, the final attack prompt word is output to a target model, and after being spliced with the attack question, the attack prompt word is returned and an analysis report is output; finally, the prompt word in the analysis report is adjusted by using an automatic mutation and expert modification strategy, and the prompt word is reconstructed and iterated according to the feedback result of each round of attack, so that a new prompt word is generated. The application combines offline evolution and online decision-making to automatically generate a high-success-rate jailbreaking prompt word, controls the length and overhead, and optimizes mutation by using semantic rewriting and logic skeleton extraction.

This invention proposes a deep learning-based method for joint generation of beam training and measurement patterns, belonging to the field of communication technology. The method includes: acquiring a full codebook beam measurement quality dataset; performing self-supervised pre-training on the dataset using a high-ratio random masking strategy to obtain a general beam prediction model; inputting complete beam measurement data into the model and calculating importance scores through backpropagation; iteratively selecting the beam measurement points with the highest importance scores using a greedy selection strategy based on a given beam training overhead constraint; updating the importance scores of the remaining beams after each round of selection by penalizing them based on the spatial correlation between the selected beams and the remaining beams; stopping the iteration and outputting the final optimal beam measurement pattern when the number of beam measurement points in the optimal pattern set reaches the beam training overhead constraint, guiding the base station to send the corresponding reference signal. It possesses strong generalization capabilities and can flexibly adapt to various measurement configurations and scenario changes.

The application provides a phased array antenna beam broadening method, electronic equipment and a readable storage medium. The method comprises the following steps: constructing an optimization fitness function suitable for a phased array antenna, wherein the optimization fitness function comprises a normalized weighted combination of a beam width term for guiding an optimized half-power beam width to approximate a target beam width and a sidelobe level term for guiding an optimized sidelobe level to approximate a sidelobe suppression reference level; setting array physical parameters and beam optimization target parameters; initializing a meta-heuristic algorithm and generating an initial population, each individual in the initial population corresponding to a phase configuration scheme of all elements in the phased array antenna; starting an iterative optimization cycle, and in the case that the fitness value of each individual in the current population does not satisfy an iteration end condition, sequentially performing elite population screening, algorithm parameter updating, mutation operation, crossover operation and greedy selection operation to obtain a population of the next iteration.

This invention discloses a relay resource allocation method and system based on a quantum flying fox optimization mechanism, relating to the field of wireless communication technology. It aims to address the problems of uneven energy consumption, short network lifetime, and poor usability in existing cooperative multi-user relay networks under high-dimensional relay nodes. The key technical points of this invention include: Step 1: Establishing a wireless sensor network system model with the goal of maximizing network throughput; Step 2: Setting the operating parameters of the cooperative multi-user relay network and initializing the network state; Step 3: Initializing the quantum flying fox population, constructing an fitness function based on the objective function, and determining the globally optimal and worst quantum positions; Step 4: Updating the quantum rotation angle of each quantum flying fox using hopping and exploration strategies to generate the quantum positions of the quantum flying foxes; Step 5: Updating the next generation of quantum positions, the globally optimal quantum position, and the worst quantum position using a greedy selection mechanism; and finally, solving for the relay resource allocation result.

This invention discloses a multi-target adaptive optimization cooperative tracking planning method based on a dynamic hybrid mutation sparrow search algorithm. It aims to solve the problems of slow convergence speed, susceptibility to local optima, and difficulty in balancing tracking accuracy, resource consumption, and redundancy in multi-radar cooperative tracking resource allocation optimization. This method constructs a multi-factor fitness function that integrates tracking accuracy, spatiotemporal redundancy, resource consumption, and decision consistency. It initializes the sparrow population and assigns roles, executes a position update strategy, calculates the fitness change rate and population diversity to dynamically select either Cauchy mutation or elite back-learning strategies, updates the population through greedy selection, and finally outputs the optimal tracking scheme. This invention effectively balances the algorithm's global search and local exploitation capabilities, improving tracking quality and resource utilization efficiency.