140 results about "Time complexity" patented technology

The invention discloses a large language model output quality guarantee method, device and system based on chain type deterministic reasoning verification, and belongs to the field of artificial intelligence safety, deterministic calculation and reasoning verification. The method comprises the following steps: classifying each reasoning step generated by LLM into a deterministic step (bT: deductive reasoning, verified facts and mathematical proof) or a non-deterministic step (bF: inductive reasoning, unverified references and creative guess), and accumulatively constructing a reasoning chain; chain certainty verification chain (chain) = foldr (AND, true, chain) and time complexity O (n) are carried out in each k steps; if the bF is returned, a chain pollution elimination theorem is applied to prove that once the chain contains non-deterministic steps, the whole chain loses deterministic guarantee; and a deterministic boundary report is generated, and a non-deterministic initial position is marked. The device comprises an inference classifier, a reference verifier, a mathematical checker and a chain verification engine, and all the modules can be independently realized (formalized verification / database query / rule engine / mixed mode).

This application provides a point cloud decoding performed by an electronic device. The method includes: obtaining a current point to be decoded from a point cloud code stream, the current point comprising N types of attribute information, and N being an integer greater than 1; determining M attribute groups of the N types of attribute information of the current point, M being a positive integer less than N; and perform uniform decoding for each attribute information in a same attribute group of the current point using a first operation, the first operation comprising at least one of a neighbor point determination and an attribute predicted value calculation. According to this application, the point cloud decoding method avoids the repeated searching, reduces time complexity during attribute decoding and improves attribute decoding efficiency.

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 application discloses a kind of combination packet classifier and method based on hash and TCAM, adopt the combination of imperfect hash classification module and TCAM classification module, the complement of imperfect hash classification and TCAM classification on input key value space is realized;Compared with the traditional TCAM packet classification solution based on, the application reduces hardware resource utilization, system power consumption;Compared with the packet classification solution based on perfect hash, the application reduces the design cost of hash function and the time complexity of generation process under the premise of guaranteeing the function of classifier.The application can consider low space resource consumption, low power consumption and low delay in the classification stage, saves design cost in the classifier generation stage, and reduces the time complexity of generation process;Meanwhile, the application can better balance different types of hardware resources on reconfigurable hardware platforms such as FPGA, and provides a new way of thinking for the design of classifier in network tasks such as firewall packet filtering and routing table lookup.

An implicit video recommendation algorithm, by calculating the proportion of the time length of the video watched by the user to the total time length of the video, then the data dispersion situation data is counted, the data is taken as the parameter, the data table of the user and the video is established, then the cosine similarity is calculated, after sorting, the video with the highest similarity is recommended, according to the accuracy, recall rate of the training result, the proportion of data division is dynamically adjusted, so as to achieve the purpose of optimizing the final recommendation result. By deeply mining the relationship between users and videos, this can solve the problem of long tail commodity recommendation and data sparsity, and the time complexity of this algorithm is low, and the operation speed is fast.

The invention discloses a time domain transformation optimization method in a vector network analyzer, relates to the technical field of signal processing, and provides an optimization algorithm for establishing a mathematical relationship between a variable time axis and IFFT (Inverse Fast Fourier Transform) based on IDFT (Inverse Fast Fourier Transform) aiming at the defects of a traditional time domain transformation algorithm in the vector network analyzer. The final result is completely consistent with the variable time axis IDFT, the time complexity is reduced to be equivalent to that of the IFFT, the calculation speed is increased by thousands of times compared with that of the IDFT when the number of data points reaches tens of thousands, and the requirement of the vector network analyzer for observing the time domain fine response can be efficiently and flexibly met.

An exemplary sliding window scheduling method and system are disclosed. The exemplary sliding window scheduling method and system can schedule multiple packets in a given scheduling frame with a sliding window scheduling frame. The scheduling operation can be performed using bitmap operators and can achieve a lowest time complexity of O(1) per matching computation and per port using distributed parallelization hardware. The exemplary sliding window scheduling method and system can be performed in the context of a queue-proportional scheduler (QPS) as well as iSLIP. In alternative embodiments, the SW-QPS operation can be performed in a batching window rather than in a sliding window.

The purpose of this invention is to address the issue of accuracy in processing spinning time-series data streams, which are characterized by high noise levels and distinct time-domain features generated during the operation of spinning workshops, by employing a fuzzy hierarchical clustering method that integrates time-domain characteristics. This method reduces the impact of noise during the classification process and considers both time-domain features and noise effects. The technical solution of this invention is to provide a fuzzy hierarchical clustering analysis method for spinning time-series data that integrates time-domain characteristics. This invention proposes a fuzzy hierarchical clustering analysis method for spinning time-series data that integrates time-domain features. It iterates between the DTS feature matrix and the MTS feature matrix, considering the time-domain characteristics and noise effects in the spinning time-series data. Without increasing time complexity, it incorporates the time-frequency characteristics and noise effects in the spinning time-series data during the iteration process. Compared with the latest methods, this invention can more accurately process newly generated time-series data in spinning manufacturing.

The invention discloses a time series data frequent pattern mining method, and belongs to the technical field of CPU utilization rate data processing. According to the invention, the problems of high calculation complexity and high power consumption of the existing method are solved. According to the method, the Boolean mask vector M is introduced, elements unfavorable for the effectiveness of the lower bound are selectively shielded, and the tightness and the filtering capacity of the lower bound are further improved. Firstly, LBDIAG with high calculation efficiency is used for carrying out preliminary filtering, then a traditional tight lower bound is used for carrying out a fine cascade filtering strategy, the number of candidate pairs entering DTW distance accurate calculation can be reduced, and the time complexity of calculation is reduced through combination of a sliding window technology and inner product operation. And lower bound calculation tasks of diagonals are allocated to different threads by utilizing the parallel calculation capability of the GPU, so that the processing speed of large-scale data is remarkably improved, meanwhile, a memory access mode is optimized, the global memory access frequency is reduced, and the power consumption in the calculation process is reduced. The method can be applied to CPU utilization rate data processing.

Disclosed in the present invention is a real-time recursive CRC method. The method comprises: sequentially extracting from a real-time bit stream a1a2a3… binary sequences S1S2…Si… at non-fixed lengths; bisecting into left and right parts each binary sequence segment Si, so as to obtain sub-sequences, and repeatedly bisecting into left and right parts the sub-sequences until the lengths of finally obtained sub-sequences are less than a preset value; and calculating a CRC check value and check matrix of each final sub-sequence, and performing level-by-level upward merging calculation on left and right sub-sequences, so as to obtain a final check value of the entire real-time bit stream. Further disclosed in the present invention are a corresponding CRC apparatus and a computer storage medium and a program for implementing the CRC method. In the present invention, a method combining iteration with recursion is used to calculate, in real time, a check value of a variable-length binary sequence stream, thereby achieving logarithmic calculation time complexity and meeting CRC operation requirements of a real-time ultra-high-speed data stream.

The present application describes a multi-key fully homomorphic encryption (MK-FHE) scheme that enables secure and efficient multi-party computation by integrating learning with errors (LWE), ring learning with errors (RLWE), and NTRU-based encryption primitives. The scheme supports dynamic key management, parallelizable bootstrapping, and low-overhead homomorphic operations. Key innovations include a hybrid product mechanism for merging ciphertexts across cryptographic structures, a single-key blind rotation algorithm optimized for Fourier domain operations, and a noise-refreshing procedure that bounds error growth during homomorphic evaluations. This scheme achieves quasi-linear time complexity relative to the number of participating parties, making it suitable for resource-constrained environments such as federated learning and secure cloud-based AI inference.

The invention provides a fair clustering method and device for large-scale data, equipment and a medium, and the method comprises the steps: constructing a to-be-classified data set after carrying out the normalization processing of the obtained to-be-clustered large-scale data, solving the to-be-classified data set through a greedy algorithm, obtaining an initial solution, and carrying out the clustering of the to-be-clustered large-scale data. And then sampling the data set, combining the sampled data set with the initial solution to form a weighted instance of the original data, executing a linear programming algorithm to obtain an intermediate solution, and iterating the intermediate solution through a local search algorithm meeting a fair condition to obtain a local optimal solution. The solution of the constant approximation ratio can be obtained within the time complexity in linear relation with the data scale, that is, the algorithm operation time is in direct proportion relation with the data scale, and compared with a traditional method, the solution efficiency and the solution quality are remarkably improved.

The invention discloses a video coding method, device and equipment and a computer readable storage medium, and the method comprises the steps: selecting an optimal prediction mode and a suboptimal prediction mode from four prediction modes: a vertical prediction mode, a horizontal prediction mode, a diagonal left lower prediction mode and a diagonal right lower prediction mode; selecting a first candidate prediction mode from a vertical rightward prediction mode, a horizontal downward prediction mode, a vertical leftward prediction mode, a horizontal upward prediction mode and a direct current prediction mode according to the optimal prediction mode and the suboptimal prediction mode; and selecting a second candidate prediction mode from the upper block prediction mode, the left block prediction mode and the most probable prediction mode. And performing video coding by using a target prediction mode determined from the optimal prediction mode, the first candidate prediction mode and the second candidate prediction mode. According to the method, the calculation time complexity is reduced, the video coding efficiency is improved, and the real-time performance of video coding is ensured.

The embodiment of the present application discloses a kind of artificial address text similarity processing method and system filled in.This embodiment of the present application removes the regular character appearing in address in a reasonable way, so as to remove the error similarity contribution caused by address regular character.Using lower time complexity, the common substring of all more than two lengths in two addresses is calculated.A designed increasing function can map the length sequence of common substring into [0,1] space, and the function reasonably describes the characteristics of human-filled address data.A kind of address clustering algorithm capable of identifying not similar but actually identical address is designed.

The embodiment of the application provides a kind of data storage method, device, equipment and storage medium, related to computer technical field, the method comprises: for the transaction processing event of to-be-processed event, corresponding parameter query event is constructed, and transaction information in to-be-processed event is saved in transaction time sequence library with parameter query event.For the parameter processing event of to-be-processed event, corresponding parameter maintenance event is constructed, and parameter maintenance event and exchange rate parameter are associated and saved in parameter time sequence library, so it is not necessary to record corresponding exchange rate information for each transaction, avoid data storage redundancy, improve the utilization of storage resources and the processing performance of system.Parameter time sequence library and transaction time sequence library store and manage data using the same time axis, so that the retrieval time complexity is lower.The storage mechanism of event binding data is used to store the time sequence data of transaction and the time sequence data of exchange rate parameter, so that the recorded time sequence data directly reflects the business processing process.

This invention discloses a method for determining conflicts between track segments, relating to the field of rail transit technology. When the first track segment and the second track segment are in opposite directions, if the starting point of the first track segment can be found along the direction of this segment along the same segment, and the search path length is greater than 0 and less than the sum of the lengths of the two track segments; or, if the first track segment and the second track segment are in opposite directions, if the ending point of the first track segment can be found along the opposite direction of this segment along the same segment, and the search path length is greater than 0 and less than the sum of the lengths of the two track segments, then the two track segments are in conflict. This invention accurately determines whether two track segments conflict. This invention only requires one search to determine whether there is a conflict, greatly reducing the judgment time and the time complexity of the algorithm; this algorithm is particularly advantageous in the rail transit industry, where timeliness is crucial.

The invention relates to the technical field of wireless communication, in particular to a low-complexity signal detection method of an MIMO-OTFS system, which comprises the following steps: setting a grouping symbol number according to a channel condition, and calculating a maximum iteration number; in each iteration: calculating a filtered received signal through the channel matrix and the received signal; constructing a matrix psi, processing a structure matrix psi S of the matrix psi by adopting an RCM algorithm to obtain a permutation matrix, and calculating a reordering matrix according to the permutation matrix; performing Cholesky decomposition on the reordering matrix to obtain a banded matrix; calculating the SINR of each symbol which is not detected at present; all symbols which are not detected at present are arranged in a descending order according to the SINR size, the first S symbols in the sorting result are taken to be detected one by one and output, and the channel matrix and the received signal are updated; judging whether the maximum iteration number is reached or not, and if not, continuing the next iteration; according to the method, the time complexity is reduced while the bit error rate performance is ensured through an iterative strategy of reliability symbol detection and interference elimination.

The invention relates to the technical field of big data analysis and quantum computing crossing, in particular to a quantum enhanced big data optimization algorithm and application thereof, and the algorithm comprises the following steps: a, carrying out preprocessing and feature extraction on a super-large-scale data set; b, mapping the optimization target problem into a quantum computing model, including but not limited to a QUBO model; c, inputting the model parameters into a quantum processor, and executing core optimization calculation by using a quantum optimization algorithm including but not limited to quantum annealing; and d, decoding the quantum calculation result, and outputting an optimal solution. When a specific NP-hard problem is processed, the potential exponential level calculation time complexity is reduced to a polynomial level through quantum parallelism, the real-time analysis capability which cannot be achieved through traditional calculation is achieved, the quantum algorithm uses the quantum tunneling effect, and the calculation efficiency is improved. The method can jump out of a local optimal solution which is liable to be trapped by a classical algorithm with a higher probability, and finds a global optimal solution.

The invention belongs to the related technical field of polar codes, and particularly relates to a variable-length polar code codeword structure construction method, which comprises the following steps: determining the code length of a group of required polar codes and the number of information bits, the code length being N0gt; n1gt; n2gt; ... gt; the number of information bits is K0gt, NM and K0gt respectively; k1gt; K1gt; k2gt; ... gt; kM; the polarization code with the code length of N0 and the number of information bits of K0 is marked as a mother code, Pi coding bits are punched from N0 coding bits of a code word structure of the mother code until the code word structure contains Ni coding bits, and under the condition that the Pi and Pj coding bits are punched, Pilt, Pi and Pj are not punched; pj, ilt; selecting and determining polarization channel sets Ai and Aj for bearing information bits in the corresponding obtained code word structures with the code lengths of Ni and Nj on the basis of a nested inclusion relation constraint; the nested inclusion relation constraint is that the time complexity and the space complexity when the polarization code is used can be reduced.

The invention belongs to the technical field of electronic design automation, and provides a statistical method of a gating register, electronic equipment and a storage medium, the method comprises the following steps: starting from a top layer module of circuit design, taking a root clock port on the top layer module as an initial node, and traversing a clock tree of the circuit design along a clock signal propagation direction; key-value pairs are maintained in a pre-created mapping container through traversal, the flattened net is stored as keys, and a clock gating unit set corresponding to the flattened net is stored as values; based on a flattening line net connected with a sink pin of a register in the circuit design, a target clock gating unit set corresponding to the register is determined in the mapping container, clock gating units in the target clock gating unit set are associated with the register, and the sink pin is a clock signal input end. According to the method, the information of the gating register can be accurately counted, and the time complexity of counting is optimized.

The application discloses an invoice automatic cancellation method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: in response to a to-be-cancelled request, obtaining a to-be-cancelled settlement sheet to determine the total amount of an output invoice set; obtaining a to-be-cancelled invoice set and a preset output invoice quantity to filter a first invoice set and a second invoice set from the to-be-cancelled invoice set, and determining the target total amount of the second invoice set; calling a preset double-pointer algorithm to determine whether there is an invoice that meets the target total amount of the second invoice set in the second invoice set; if yes, adding the invoice to the first invoice set and outputting the invoice to execute the processing of the to-be-cancelled settlement sheet; if not, re-filtering the first invoice set until the output invoice set is obtained to execute the processing of the to-be-cancelled settlement sheet. Thus, the embodiment of the application can solve the technical problems of high time complexity and low realizability of the existing financial side invoice cancellation.

The invention discloses a multi-sequence parallel comparison method based on maximum precision matching, and relates to the technical field of crossing of bioinformatics and computational biology. The method comprises the following steps: splicing character strings of a plurality of sequences to obtain a long sequence, and determining a suffix array and a longest common prefix array of the long sequence by adopting a mode of combining a bucket dividing strategy and induced sequencing; according to the suffix array, searching a maximum accurate matching fragment from the longest common prefix array, and screening an ordered and non-overlapped maximum accurate matching fragment set through a dynamic programming technology; expanding each maximum accurate matching fragment in the maximum accurate matching fragment set to all sequences to form a chain; segmenting the long sequence through a chain to obtain a plurality of sub-sequence sets; and calling third-party multi-sequence comparison software to compare the plurality of sub-sequence sets, and splicing the comparison result of each sub-sequence set to obtain a multi-sequence comparison result. According to the method, the time complexity of ultra-long or large-scale sequence data comparison is reduced.

Embodiments of the present disclosure include systems and methods for reducing a sample complexity and a time complexity associated with noise-robust characterization of a quantum device. A plurality of copies of a Gibbs state of the quantum device in thermal equilibrium at a high-temperature. A plurality of estimates for expectation values of the plurality of copies of the Gibbs state. A plurality of cluster derivatives for a plurality of connected clusters of a low-degree Hamiltonian are calculated. A function is inverted on the plurality of estimates based on the plurality of cluster derivatives and a set of Hamiltonian coefficients are estimated for the low-degree Hamiltonian of the quantum device.

The invention discloses a three-dimensional flight trajectory data clustering storage and query method and system oriented to efficient query. According to the method, track feature extraction and K-means clustering are utilized to carry out fine bucket separation according to track feature vectors, and semantically similar tracks are physically stored in a centralized manner; during query, a few candidate buckets are quickly positioned through the K-means cache and the tree cache, so that global scanning is avoided, the query time complexity is reduced, and the retrieval speed is greatly improved. According to the method, multi-dimensional features such as longitude and latitude are extracted from an original track and coded into a uniform feature vector, so that subsequent storage, clustering and query can naturally support mixed retrieval based on spatial positions and dynamic features, and the limitation that a traditional scheme can only process two-dimensional space or ignore dynamic attributes is overcome. According to the method, the clustering storage of the three-dimensional flight path data can be realized, the efficiency of range query and neighbor query is remarkably improved, the write-in throughput is improved, and the query delay is shortened.

The present disclosure belongs to the field of computer, and more particularly relates to an operation device and method, the operation device comprising: an operation control module, used for receiving or determining block information; an operation module, used for obtaining a block matrix by block processing an operation matrix according to the block information, and obtaining a transpose matrix of the block matrix by transposing the block matrix according to an operation instruction. The device and method provided by the present disclosure can realize the operation of a matrix of any size within constant time complexity by using a single instruction. Compared with the traditional operation operation implementation method, the operation time complexity is reduced, and the operation operation is also more simple and efficient.

The invention discloses a category subordination relation distinguishing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: traversing category nodes in a category tree; placing the category nodes into an array to form a category array; obtaining a to-be-discriminated category identifier and a category identifier of a specific category node in the category tree, determining a sequence number range corresponding to a hierarchy in the category array according to the hierarchy of the specific category node in the category tree, and determining a specific sequence number of the specific category node in the sequence number range; obtaining a target serial number of a subordinate category node of the specific category node in the category array according to the specific serial number and the structure parameter of the category tree, and determining the subordinate category node in the category array by using the target serial number; and in response to the fact that the to-be-discriminated category identifier is consistent with the category identifier of any subordinate category node, determining that the to-be-discriminated category belongs to the specific category node. According to the embodiment, the time complexity of category affiliation calculation can be reduced.

The invention relates to a multi-time-period overlapping detection method and system. The method comprises the steps that S1, multiple time period data input by a user are obtained; s2, uniformly converting the multiple pieces of time period data into standard UTC time; s3, judging whether each time period crosses the day or not; and S4, judging whether the plurality of time periods are overlapped or not. For scenes with continuous time periods such as scheduling, the step S4 comprises the following steps: S41, sorting the starting time of N time periods T according to a sequence to obtain T1, T2... Tn; s42, overlapping detection is carried out on the time period Ti and all time periods in a sliding window, overlapping information is obtained, and i is larger than or equal to 2 and smaller than or equal to N; the sliding window comprises all time periods of which the end time is later than the start time of the time period Ti. Compared with a traditional overlap detection algorithm, the time complexity of the detection method is only O (NlogN), and consumption of computing resources is reduced.

The application discloses a dynamic abnormal subgraph discovery method in a time sequence financial network. The method comprises the following steps: obtaining a dynamic financial network, wherein the dynamic financial network comprises nodes, edges between the nodes, and edges between the nodes are used to represent transaction information between at least one of the nodes; estimating the dynamic financial network through an approximate linear time complexity to obtain dense subgraphs of different time slices, performing chi-square calculation on each edge of the dense subgraph of each time slice to obtain the chi-square of each edge of each time slice, and reserving nodes corresponding to edges with chi-square greater than or equal to the total chi-square of each time slice in the dense subgraph of each time slice. The application solves the technical problem that the prior art can only identify static abnormal subgraphs in a financial network and cannot detect changes and trends of user transaction behaviors over time.

The invention discloses a data asset generation and matching method based on physical asset token coinage, and the method comprises the steps: carrying out the standardized definition of a data model for RWA data assets and blockchain upper chain smart contracts, including the standardized definition of data and the standardized definition of the smart contracts; the method comprises the steps of receiving retrieval data submitted by a user by constructing an asset matching model, and converting the retrieval data into standardized matching dimension parameters; a double-layer index structure of a global main index and in-chain sub-indexes is constructed, and the retrieval time complexity is reduced through distributed indexes; a two-stage algorithm of rough matching filtering and fine matching sequencing is adopted to balance the matching efficiency and accuracy; a cross-chain adapter is constructed based on the matching requirement of the multi-chain assets, and synchronization of metadata format differences of different chain assets and the state of the cross-chain assets is achieved. The method can be well compatible with a cross-chain environment, the matching efficiency of RWA data assets is improved, and meanwhile high matching precision can be kept.

The invention relates to an automatic program optimization method and system for synthesizing and eliminating an intermediate data structure by utilizing an induction program. The method comprises the following steps: acquiring a low-efficiency program which is input by a user and contains an intermediate data structure, and appointing the intermediate data structure which needs to be eliminated; a specified intermediate data structure is replaced by a scalar type, and related program fragments in the original program are rewritten using efficient program fragments of constant time complexity. The invention provides a novel induction program synthesis method aiming at the problem of eliminating an intermediate data structure, the induction program synthesis problem can be efficiently decomposed into simpler subtasks, so that the confronted efficiency problem is overcome, and the method has stronger expression ability than an existing fusion method, and can be widely applied to the field of data fusion. And the time for a programmer to carry out efficient system development can be greatly reduced.