8results about How to "Guaranteed Search Accuracy" patented technology

The application belongs to the field of workshop scheduling, and particularly discloses a semiconductor wafer probe test scheduling method and system based on an improved genetic algorithm, which adopts double-layer process-machine coding, and combines auxiliary resource taboo coding as an individual, effectively converts a complex scheduling problem into a coding form through an innovative coding and decoding strategy, and avoids traversal search on all selectable auxiliary resources; global-local hybrid search is carried out through the improved genetic algorithm, on the basis of machine level exchange with the traditional critical path-key block, search on the auxiliary resource immediately preceding work is increased, and neighborhood exploration is carried out based on a load balancing strategy, so that the search efficiency and solution quality are improved. The application can quickly and efficiently solve the semiconductor wafer probe test scheduling scheme, effectively improve the equipment utilization, speed up the scheduling efficiency, and break through the bottleneck of traditional scheduling.

The invention provides a cell initial access-oriented multi-dimensional parameter estimation method and system, a medium and a receiver. The method comprises the following steps of: receiving a received signal from at least one network node; generating a local reference signal corresponding to each network node signal; for each network node signal, acquiring a rough candidate parameter area based on the received signal and a corresponding local reference signal in a preset multi-dimensional parameter space; and in the rough candidate parameter region, searching the first-dimensional parameter based on a divide-and-conquer method to obtain a matched multi-dimensional parameter estimation value. According to the cell initial access-oriented multi-dimensional parameter estimation method, the cell initial access-oriented multi-dimensional parameter estimation system, the medium and the receiver, high-precision estimation of multi-dimensional parameters can be quickly and efficiently completed.

The invention discloses a micro-grid scheduling optimization method and system based on a multi-objective parrot-shaped Arctic pole optimization algorithm, and the method comprises the steps: building a multi-objective optimization scheduling model of a micro-grid, and the multi-objective optimization scheduling model at least comprises an operation maintenance cost objective function and an environment protection cost objective function; a multi-strategy fused multi-target parrot-shaped north pole optimization algorithm is adopted to solve the multi-target optimization scheduling model, and a Pareto optimal solution set is obtained; and determining an optimal output strategy of each distributed power generation unit in the microgrid according to the Pareto optimal solution set. The space exploration capability of the algorithm in the early stage and the optimization precision of the algorithm in the later stage are ensured, and the optimization effect of micro-grid dispatching is improved.

This invention relates to the field of neural network architecture search and industrial intelligent processing technology, and discloses a structure search method for multi-output dendritic neuron models for industrial classification tasks. The method includes: initializing the synaptic connection weights and dendritic threshold parameters of the multi-input multi-output dendritic neuron model to generate an initial population; uniformly dividing the initial population into several subpopulations of equal size; calculating the temporal and spatial criteria for each subpopulation after task allocation and optimization, and dynamically allocating evolutionary computational resources for each subpopulation based on the fitness change rate represented by the temporal criterion and the population distribution state represented by the spatial criterion; implementing an accelerated sharing penalty mechanism to adjust fitness values ​​according to the crowding degree among individuals to maintain solution set diversity; and updating each subpopulation. This invention solves the problem of low convergence efficiency caused by uniform resource allocation in traditional large-scale multi-objective evolutionary algorithms during neural network architecture search.

This invention discloses a method for adjusting the end effector stiffness of a hybrid robot used in the machining of thin-walled structural parts. The method first collects the proportional gain of each active branch servo drive system as the axial servo stiffness; secondly, it establishes a mapping model based on the robot configuration to solve for the equivalent axial servo stiffness of the end effector in the reference coordinate system; subsequently, it introduces stiffness weighting coefficients and combines them with the mechanical stiffness and electromechanical coupling stiffness performance evaluation index; finally, it uses a one-dimensional discrete search algorithm to traverse and filter redundant rotation angles that satisfy displacement and rotation constraints while keeping the end effector pose unchanged, aiming to maximize the electromechanical coupling stiffness index and generate an optimal sequence of redundant rotation angles. This invention establishes an electromechanical coupling model integrating mechanical and servo systems and adaptively adjusts the end effector stiffness using redundant degrees of freedom, which can effectively suppress cutting deformation and chatter during the machining of thin-walled parts while reducing computational complexity, significantly improving machining quality.

A method and system for image similarity recognition based on multi-feature fusion, relating to the field of image processing technology, is disclosed. The method includes: calculating the hash features of an input image and generating hash similarity and hash confidence values; when the hash confidence value is less than a hash confidence reference value, performing local feature extraction on the input image to generate local similarity and local confidence values; when the local confidence value is less than the local confidence reference value, performing deep learning feature extraction on the input image to obtain deep features and deep confidence values; determining the weight coefficients of the hash features, local features, and deep features based on the hash confidence value, local confidence value, and deep confidence value, respectively; performing feature fusion to generate a fused feature vector; and calculating the final similarity between the input image and the image to be compared based on the fused feature vector. Implementing this application can improve processing efficiency while ensuring the accuracy of image similarity recognition.

The application discloses a data intelligent retrieval and access method and system in the field of space science, and the method comprises the following steps: according to the text input by a user, adopting a word segmentation model and a word embedding model respectively, mapping the text to corresponding key words and embedding vectors, and forming double-path recall retrieval results of a data set based on key word matching and embedding vector similarity matching; based on a rearrangement model, reordering the two kinds of retrieval results of the data set to form a query result of the data set granularity; according to the text input by the user, a data sample and a selected data set, adopting a pre-constructed machine feature extraction model, extracting a vector representation of user retrieval information, and based on vector similarity matching, retrieving data files in the data set to form a query result of the data file granularity; based on a pre-established optimal affine transformation function and a metric space, quantitatively calculating the data similarity with respect to a retrieval sample in terms of distance to obtain a retrieval result.

The invention discloses a digital information processing method and system based on mixed retrieval, and aims to solve the problems of low retrieval efficiency and poor precision of a knowledge base in a professional field. The method comprises the following steps: firstly, constructing two indexes for a document set in parallel, wherein one index is a vector index for recording semantic vectors of document blocks, and the other index is an inverted index based on a preset regular expression; after user query is received, the system starts two retrieval paths in parallel: one is that global semantic retrieval is executed through vector indexes to obtain a preliminary candidate set; and 2, analyzing a specific mode in query, and if the specific mode is matched with the regular expression, performing rapid and accurate matching by using an inverted index to obtain a candidate subset with a smaller range. And the system preferentially performs fine sorting in the subset in combination with semantic similarity and outputs a result. According to the method, by introducing regular rapid preliminary screening, the follow-up processing range is effectively narrowed, and the retrieval efficiency and the retrieval precision of information such as specific codes and identifiers are remarkably improved.