40 results about "Clustering search" patented technology

In an embodiment, operations include receiving a dataset including a set of graphs, applying a graph explainer model on the dataset to determine explain scores, and filtering graph elements of each graph based on the explain scores to determine a first set of graph elements. Further, another dataset of randomized graphs is generated. The graph explainer model is applied on the other dataset to determine noise scores, which are used determine denoised explain scores from the explain scores. Graph elements of each graph are filtered based on the denoised explain scores to determine a second set of graph elements. Thereafter, feature vectors are determined for the first set of graph elements and the second set of graph elements, and prediction driving graph elements are determined based on the feature vectors. Explainability information associated with the set of graphs is rendered, based on the prediction driving graph elements.

The invention provides a voice recognition method and system based on an AI large model, and belongs to the technical field of voice recognizing.The method comprises the steps that firstly, noise reduction and frequency band enhancement in a noise environment are achieved through a pre-trained anti-noise suppression network, and a basis is provided for follow-up processing in combination with multi-dimensional spectrum quality scores; based on similarity matching of element feature vectors and a pre-constructed dialect thermodynamic diagram library and matching weight adjustment based on frequency spectrum quality scores, accurate modeling of specific dialect pronunciation deviation is achieved, moreover, through an acoustic adaptation matrix and a language model adaptation matrix generated through a super network, the adaptability of the model to different dialects is improved, and in addition, the accuracy of the model is improved. According to the method, a fusion thermodynamic diagram and an acoustic adaptation matrix are jointly injected into a pre-trained acoustic model, the recognition accuracy of dialect phonemes is improved through multi-level attention correction, and finally, accurate speech recognition is achieved by adopting a thermodynamic diagram guided cluster search algorithm and combining verification of an adversarial discrimination network.

The invention relates to the technical field of glue pudding quality detection, and discloses a glue pudding quality intelligent detection system and method based on multi-sensor fusion. The system comprises a data acquisition module, a stability analysis module, a detection parameter search module, a quality detection execution module and a quality judgment module. The data acquisition module acquires a surface image, weight and texture elasticity data set of the glue pudding sample through a multi-sensor array; the stability analysis module evaluates the acquired data to obtain image, weight and texture elastic stability factors; the detection parameter search module takes each stability factor as a joint index, and determines an optimal detection parameter group through density clustering search in a detection parameter space; the quality detection execution module detects the target glue pudding of the production line according to the optimal parameter group to obtain multi-source detection data; and the quality judgment module inputs the multi-source data into a classifier to obtain a quality grade judgment result. The system realizes comprehensive detection of the glue pudding quality, and improves the detection intelligence and automation level.

The invention provides an unmanned aerial vehicle cluster distributed region search method and system based on role differentiation. The method comprises the following steps: initializing an unmanned aerial vehicle collaborative search task environment; the information unmanned aerial vehicle updates a cluster search cognitive map; task allocation modeling of unmanned aerial vehicle cluster collaborative search; unmanned aerial vehicle tasks are pre-allocated based on CBAA information; updating a cluster topology connection state of the information subgroup; relay unmanned aerial vehicle task allocation based on connectivity maintenance; unmanned aerial vehicle waypoint tracking control based on collision time cooperative guidance; adjusting an information-relay subgroup proportion based on a region coverage rate; and outputting an unmanned aerial vehicle cluster collaborative search result. According to the method, the CBAA algorithm supports the communication of the unmanned aerial vehicle cluster to keep a distributed decision through improved strategies such as removal of disconnected individuals, an action selection tabu table and mobile base station switching; meanwhile, heterogeneous cooperation between information-relay subgroups is utilized, the action space of the unmanned aerial vehicle is released, the persistent exploration capability of a later cluster is enhanced, and local optimum of a search process is avoided.

The invention belongs to the technical field of data search, sharing and encryption, and relates to a BK-LSH-based searchable encrypted data sharing method, system and terminal. The sharing method comprises the following steps: processing original data to obtain an abstract and a data ciphertext; the method comprises the following steps: extracting keywords of original data, constructing a hash table represented by keyword vectors, grouping hash buckets in the hash table into a plurality of initial clusters, performing iterative optimization, and taking a clustering result as a search index of the original data; uploading the abstract, the data ciphertext and the search index to a cloud server; configuring a plurality of query keywords, generating query trapdoors, uploading the query trapdoors to the alliance chain, positioning corresponding hash buckets in the hash table, matching the query trapdoors with keyword vectors processed by hash functions in the hash buckets to obtain matching degree scores, sorting search indexes according to the matching degree scores from high to low, and storing the search indexes in the alliance chain. And outputting the abstracts and the data ciphertexts corresponding to the previous plurality of search indexes, and decrypting the data ciphertexts to obtain original data. The method is high in clustering search performance and safety.

The invention provides a multi-granularity code clone detection method based on clustering search and a large model, and belongs to the technical field of code clone detection. Comprising the steps that an MGCD analyzes a code library to generate a vector database, and each vector represents a function-level code snippet; the code to be detected is analyzed, and the MGCD identifies function clone candidate items in a database by using a search algorithm; an automatic heuristic method is used as a filter to obtain a highly correlated and accurate function-level result; and the MGCD realizes multi-granularity clone detection. According to the method, the semantic clone recognition capability is remarkably enhanced, and complex code snippets with similar semantics are effectively detected. The clustering algorithm improves the retrieval efficiency in a large-scale code library and reduces the calculation cost. The multi-granularity code clone detection is supported, multiple levels such as a function level, a module level and a project level are covered, a more comprehensive and multi-dimensional code similarity analysis result is provided for a user, the detection efficiency is improved, and the flexibility and expansibility of the system are enhanced.

In an embodiment, operations include receiving a dataset including a set of graphs, applying a graph explainer model on the dataset to determine explain scores, and filtering graph elements of each graph based on the explain scores to determine a first set of graph elements. Further, another dataset of randomized graphs is generated. The graph explainer model is applied on the other dataset to determine noise scores, which are used determine denoised explain scores from the explain scores. Graph elements of each graph are filtered based on the denoised explain scores to determine a second set of graph elements. Thereafter, feature vectors are determined for the first set of graph elements and the second set of graph elements, and prediction driving graph elements are determined based on the feature vectors. Explainability information associated with the set of graphs is rendered, based on the prediction driving graph elements.

The invention relates to a cluster search method and device, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining a translation request of a user for a to-be-translated text of a source language, the translation request comprises words specified by the user, and if the specified words comprise at least two words, the to-be-translated text is translated; generating a first target language combination, a first candidate target language combination and a second candidate target language combination based on the initial word, the at least two words and the to-be-translated text, and determining a target translation result corresponding to the to-be-translated text according to a probability score corresponding to each sentence in an initial candidate sentence set formed based on the first target language combination, the first candidate target language combination and the second candidate target language combination. By means of the method, in the process of translating the to-be-translated text, the specified words can be added into the translation process, and the translation requirement of a user is met.

The invention relates to the technical field of intelligent power grids, and particularly provides a power grid intelligent decision-making method, system and device, a medium and a computer program product, and the method comprises the steps: dividing an intelligent agent cluster suitable for a target power grid; establishing an equivalent model of the agent cluster, and generating a decision model of an independent agent; in response to a power regulation and control instruction issued by the power grid coordination center, the target agent cluster searches a matched candidate agent cluster in the regional hierarchy; and the independent intelligent agent determines a matched target intelligent agent, and performs power adjustment according to the energy output information of the target intelligent agent in a preset historical duration. According to the technical scheme provided by the invention, the overall scheduling flexibility of the power grid system can be improved through the intelligent power grid system.

Methods and apparatuses for automatically generating and displaying clustered search results and contextual recommendations are described. While a user is making edits to an electronic document or within an application, a search system may identify a set of search terms based on information entered by the user and / or the locations of the information entered by the user within the electronic document or application and then generate and display search results based on the set of search terms. The search system may cluster search result documents that have a degree of similarity and display multiple clusters of search results to the user. The search system may allow users to specify personalized keyword links and may return a personalized keyword link as a search result for a given search query even if a document or resource referenced by the keyword link has not been indexed or crawled.

The invention relates to a KAN-based unmanned vehicle trajectory prediction method. The method comprises the steps of 1, screening unmanned vehicle threat distribution diagrams obtained according to unmanned aerial vehicle cluster search information to obtain a target probability distribution diagram; 2, extracting data set features through a high threat region weighted midpoint method or a clustering-based position extraction method to obtain a target position; and 3, selecting a proper activation function, and predicting a target trajectory by using the double-KAN prediction model. Compared with an existing trajectory prediction algorithm, the method has the advantages that feature extraction is carried out on the target threat distribution map, and the explicit expression is obtained through KAN training, so that the interpretability of target and trajectory search of the unmanned aerial vehicle cluster is further improved.

This invention discloses an energy-saving control method for pump sets based on discrete operating point clustering search optimization, belonging to the technical field of pump set scheduling control. It acquires pump set operating data based on sensors, predicts the target flow rate based on historical flow time-series data, and determines the target head. Under the premise of meeting water supply constraints, the frequency of any one or more pumps in the pump set is perturbed, and discrete operating points under different frequency combinations are collected. Clustering search optimization is performed based on the discrete operating points to obtain candidate clusters that meet the target requirements. Within the candidate clusters, a multi-index comprehensive evaluation model is constructed to score the points within the clusters, and the points within the clusters with higher scores are selected as representative points, obtaining a set of representative points. Based on the set of representative points, the representative point that best meets the target requirements and has the lowest adjustment cost is selected as the optimal operating point. This invention directly uses sampled points for optimal solution search, avoiding fitting errors and more closely reflecting actual operating conditions, thus possessing good practicality.

The invention provides an unmanned aerial vehicle cluster distributed region search method and system based on global connection. The method comprises the following steps: initializing an unmanned aerial vehicle collaborative search task environment; the unmanned aerial vehicle updates a cluster search cognitive map; performing task allocation modeling of multi-unmanned aerial vehicle collaborative search; cooperative search pre-distribution based on CBAA is carried out; carrying out collaborative search redistribution based on global connectivity; unmanned aerial vehicle waypoint tracking control based on collision time cooperative guidance; and outputting an unmanned aerial vehicle cluster collaborative search result. According to the method, distributed decision-making among multiple unmanned aerial vehicles is realized by using CBAA, and a suboptimal action strategy of the unmanned aerial vehicle is obtained by relying on the optimal suboptimal solution characteristic of the CBAA in a multi-agent task allocation problem; according to the method and the system, the unmanned aerial vehicle cluster is divided into a plurality of unconnected individuals, an unconnected individual removal and action selection taboo table and a mobile base station switching strategy are introduced, so that the CBAA can effectively exclude action strategies of the unconnected individuals, and the search efficiency and the coverage efficiency of the unmanned aerial vehicle cluster are effectively improved on the premise of ensuring global connectivity by releasing action spaces of decision lagging individuals.

The present application relates to the technical field of photovoltaic power station digital modeling, and more particularly to a photovoltaic string reverse modeling method based on laser point cloud and geometric feature extraction, comprising: collecting high-precision three-dimensional laser point cloud data of a photovoltaic power station, using a deep learning model to perform instance segmentation on the point cloud data, accurately extracting photovoltaic strings and establishing a unique index, identifying the geometric features and contour point set of the photovoltaic strings through vertex extraction, distance-based cluster search and angle-based contour detection methods, calculating the inclination and direction angle of the photovoltaic strings using an interpolation fitting method, calculating the center position of the photovoltaic strings using the contour point set, detecting and calculating the area of the photovoltaic panel shielding or damage using a sliding window method, and finally completing the reverse modeling of the photovoltaic power station. This method significantly improves the anti-interference capability and modeling accuracy of data acquisition, reduces the consumption of computing resources, and is suitable for intelligent management and operation of large-scale photovoltaic power stations under various environmental conditions.

The application discloses a ruled surface and plane approximation method of NURBS surface, comprising: carrying out equal parameter line discretization and surface slicing pretreatment on a target NURBS surface; adopting a straight line cluster search technology based on principal component analysis method to obtain an initial straight line cluster approximating the NURBS surface; utilizing the initial straight line cluster to construct an initial ruled surface, and carrying out overall optimization on the initial ruled surface based on a least square method to obtain one or a group of ruled surfaces meeting the approximation error requirement. The application converts a complex NURBS surface into one or a group of ruled surfaces through three steps of pretreatment, straight line cluster search and overall optimization, and is suitable for efficient rough machining and tool path planning.

The invention discloses a three-dimensional track reconstruction method and device for muon imaging, and relates to the field of signal detection and ray imaging, and the method comprises the steps: obtaining an original waveform signal of muon imaging, and carrying out the preprocessing of the original waveform signal; performing channel waveform analysis on the preprocessed original waveform signal by adopting a waveform identification technology to obtain a channel waveform analysis result; in combination with a detector-electronics channel mapping table, decoding continuous channel information by utilizing hit continuity characteristic channel clustering; on the basis of the continuous channel information, utilizing a spatial point clustering algorithm to take spatially adjacent electronic signals in the continuous clusters as the same electronic cloud cluster; and based on the three-dimensional barycentric coordinates of the plurality of electronic clouds, completing three-dimensional track reconstruction by using a screening reconstruction algorithm. According to the method, the problem of fuzzy reconstruction of a small-angle incident muon example is solved through channel waveform analysis, hit cluster searching and three-dimensional track reconstruction.

The application discloses an optimal flyby sequence acquisition method based on a parallel architecture and belongs to the field of orbit design and optimization. The application realizes the method as follows: a parallel cluster search algorithm is constructed, and a Lambert problem is solved in parallel; a state of a spacecraft at a starting moment of a task time window is determined, a parallel cluster search is performed to obtain a sub-sequence of target access; then, the sub-sequence is saved to a database, a state and a moment at an end of each sub-sequence in the database are extracted, and the parallel cluster search is performed again from the state; the above process is repeatedly iterated to generate a sub-sequence database; the sub-sequences are spliced to generate a complete target access flyby sequence trajectory; and the total speed increment of the flyby sequence is further reduced by adjusting a transfer time between targets and optimizing a flyby speed. The application can significantly improve flyby sequence planning efficiency, effectively guarantee the optimality of sequence generation for a large-scale target problem, and acquire a high-precision and low-energy-consumption spacecraft flyby sequence.

The invention relates to the field of data processing, and relates to an e-commerce landing page intelligent generation method and system, and the method comprises the steps: obtaining a uniform resource locator of a target commodity, and collecting the multi-modal data of the target commodity based on the uniform resource locator; performing feature extraction on the multi-modal data to obtain structured style feature data including a visual feature vector and a semantic feature vector; a design space generated by the landing page is constructed, the design space is modeled into a directed graph structure, nodes represent page component modules, and edges represent connection relations between adjacent component modules; and performing path search in the directed graph structure by using a cluster search algorithm to obtain an optimal design scheme, and generating a target e-commerce landing page based on the optimal design scheme. By means of the method, the efficiency and quality of landing page generation can be effectively improved.

The invention provides a vector search system for a vector database. A cluster search module and a vector search module receive query vectors input by an application; data transmission is carried out between the cluster search module and the vector capture module, the cluster search module is used for realizing cluster sampling calculation of the FPGA hardware processor, and the vector capture module is used for realizing end-to-end access of the FPGA hardware processor; data transmission is carried out between the vector search module and the global retrieval module, the vector search module is used for realizing vector search calculation of the FPGA hardware processor, and the global retrieval module is used for realizing sorting calculation of the FPGA hardware processor; and the software runtime module is used for assisting the vector capturing module to realize end-to-end access. The end-to-end approximate nearest neighbor search speed of the system is 1.41-171.28 times faster than that of an existing system, and meanwhile, more than 90% of CPU computing resources are saved.

The present invention relates to the field of biopharmaceutical technology, and discloses a method, device, computing device and storage medium for messenger ribonucleic acid sequence design. Based on a given 5' UTR sequence as the starting input, the method adds new codons one by one using the growth method, and calculates and scores the full-length minimum free energy and codon usage efficiency of the overall sequence for sorting. The elimination method is used to exclude sequences containing restriction enzyme sites / undesirable properties / or poor scores, and then the clustering search method is used to select representative sequences to increase diversity, and finally the sequences are obtained for recommended synthesis. The present invention proposes an mRNA sequence optimization design method in the environment of 5' UTR and 3' UTR, taking into account the full-length minimum free energy of the mRNA sequence and the codon usage efficiency of the CDS region, which can optimize these two objectives simultaneously, can optimize its stability from the perspective of the overall molecule, and at the same time optimize the expressibility of the CDS region, ensuring the stability and expressibility of the mRNA sequence.

The application provides an AI image recognition front-end code generation method and system and a computer storage medium. The generation method includes three main steps of image recognition, layout analysis and code generation. The original webpage prototype and its configuration information are processed by a convolutional neural network, an optical character recognition technology and a target detection algorithm to extract interface elements and determine the hierarchical structure. Then, attribute information extraction and rendering tree generation are performed. Finally, data tokenization processing is performed to map into HTML, CSS or JavaScript code, and a greedy search or clustering search strategy is flexibly selected according to user requirements to generate the final front-end code. The system supports multiple front-end frameworks, ensures efficient and accurate code generation, and allows user interaction to enhance the individuality and adaptability of code generation. The application relates to the field of artificial intelligence technology.

The invention provides an unmanned aerial vehicle cluster distributed hybrid collaborative search method and system. The method comprises the following steps: judging a search stage of an unmanned aerial vehicle cluster search task; in the dispersed search stage, each unmanned aerial vehicle is responsible for searching a sub-region; correcting the information graph; performing single machine decision-making to obtain an optimal decision-making path; in the cluster collaborative search stage, all the unmanned aerial vehicles search the task area together; forming one or more connected subnets by the unmanned aerial vehicle cluster according to the communication range of the unmanned aerial vehicles; information graph correction is carried out based on the Bayesian criterion; evaluating the cognitive error level of each unmanned aerial vehicle; performing information fusion in the connected subnet by considering a cognitive error level; each unmanned aerial vehicle generates a pre-decision path through single-machine decision in advance; the unmanned aerial vehicles in the subnet perform cluster coordination decision making to obtain an optimal decision path of each unmanned aerial vehicle; according to the rolling planning architecture, each unmanned aerial vehicle executes one step according to the optimal decision path of the unmanned aerial vehicle and then enters the next planning step.

A feature information comparison system includes a feature information extractor configured to extract feature information from each of a plurality of multimedia data, a quantization processor configured to quantize the extracted feature information and store it in a feature information database, a clustering processor configured to cluster a set of target multimedia data using the feature information, calculate centroid for each cluster, and store the centroids in a cluster database, a cluster searcher configured to compare the quantized feature information extracted from a search input with the cluster centroids in the cluster database, select similar clusters based on the comparison, and designate the multimedia data in the selected clusters as search candidates, and a search result provider configured to compare the feature information of the multimedia data in the selected clusters with the feature information of the search input and provide a final search result based on the comparison.

The present application discloses a method, system, device and storage medium for planning an unmanned cluster search and rescue mission, which relates to the field of search and rescue technology. The method includes: analyzing multi-source distress signals in the search and rescue area to determine the accident point information; dynamically updating the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; determining the current task planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; determining the regional search and rescue priority list of each unmanned equipment based on the current task planning strategy and the target discovery probability of the grid sub-area, and performing task allocation for the unmanned cluster. Through the above method, combined with environmental features and target movement features, unified task planning and path allocation are performed for multiple types of unmanned equipment, which quickly covers the accident point, improves the search and rescue efficiency, and increases the success rate of the search and rescue mission.

This invention discloses a clustering search method and system under time-series constraints, comprising: acquiring raw time-series data and constructing a time-series graph based on the raw time-series data; initializing the node attributes of each node in the time-series graph; traversing the nodes and determining whether a node is similar to its neighboring nodes; if similar, updating the similarity of the node and the similarity of its neighboring nodes; using the degree to indicate the number of neighboring nodes of a node; connecting neighboring nodes with nodes through edges; identifying nodes with a similarity not lower than a threshold for the number of core nodes as core nodes, and clustering based on the temporal structure similarity with the core nodes as the center to obtain structural clustering results. The above scheme can quickly locate and return complete clustering results related to the search target from the clustering structure when a search condition is received, thus improving search efficiency.

The application discloses a power transmission section searching method based on variable scale near neighbor propagation clustering, and steps of the method comprise the following steps: 1, establishing a power network model; 2, searching a power transmission section by variable scale near neighbor propagation clustering. The application considers the demand of the power network, divides the whole power network in different scales by performing multiple variable scale partition operations on a power topology diagram, searches more potential power transmission sections, is beneficial to a dispatching department to timely adjust an operation mode, thereby can avoid large-area power grid outage accidents, and guarantees safe and stable operation and optimal dispatching of the power grid.

The application discloses a maximum bipartite cluster search method and system based on local and global structure feature difference measurement, calculates the chi-square value of nodes according to input bipartite graph data; maps the chi-square value of two end nodes to adjacent edges; constructs a search space according to the large edge in front of the chi-square value; executes a maximum bipartite cluster search algorithm on the search space, and obtains a result. The application can effectively model based on local structure and global structure feature difference, and take the local structure with large difference as a heuristic search space, and then obtain a result through an optimized maximum bipartite cluster search algorithm. The method not only has high search efficiency, but also can improve 1-3 orders of magnitude compared with existing heuristic methods, and the result is better, and the method has strong adaptability and can meet the maximum bipartite cluster search demand in various scenes.

The invention relates to a scaling experiment method and system based on an unmanned aerial vehicle search algorithm, belongs to the field of virtual simulation, and solves the problems of high cost and high risk of full-size offshore experiment and insufficient credibility of pure digital simulation in the existing method. Comprising the following steps: scaling prototype parameters of an offshore full-size scene to a lake surface experiment scene according to a selected scaling coefficient; in the lake surface experiment scene, the unmanned aerial vehicle cluster executes a search task on a simulation target based on a predetermined collaborative search algorithm; task data of each round of search task executed by the unmanned aerial vehicle cluster in the lake surface experiment scene is collected, and lake surface performance data is obtained; and deducing the lake surface performance data to an offshore full-size scene to obtain a performance evaluation result of the collaborative search algorithm in the offshore full-size scene. Through a scaling experiment with low cost and high controllability, the performance of the unmanned aerial vehicle cluster search algorithm in a full-size sea area can be effectively and reliably predicted and evaluated, and the research and development cost and risk are remarkably reduced.

The technical scheme of the present application discloses a real-time closed-loop detection framework and an ultra-fast descriptor generator based on FPGA, characterized by comprising a descriptor generation module and a retrieval verification module; the descriptor generation module comprises a preprocessing submodule, a clustering submodule and a descriptor calculation submodule which are sequentially cascaded; the retrieval verification module comprises a retrieval submodule based on a bag-of-words method and a verification submodule based on global descriptor cross-validation. Compared with the prior art, the clustering search space of the present application is reduced by about 2000 times. The present application eliminates the blocking problem caused by data dependence through a non-blocking architecture, and reduces the descriptor calculation complexity through a cylindrical filter and fast direction calculation. During retrieval, the technical scheme disclosed by the present application only needs 5 local descriptors to maintain high accuracy, realizes a performance improvement of 128.8 times, a single-frame processing time of 16.5 milliseconds, meets the real-time requirement of 50 milliseconds, and the energy consumption is only 21.6 mJ / frame.