64 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).

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.

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.

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 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 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.

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 embodiment of the invention discloses a coding and decoding method, a code stream, a coder, a decoder and a storage medium, applied to the decoder, the method comprises the following steps: decoding the code stream, and determining a target filtering mode of a current block; determining a reference region of the current block according to the size parameter of the current block and the target filtering mode; determining a filter coefficient of the current block according to the reference region of the current block; and performing intra-frame prediction on the current block according to the filtering coefficient, and determining a predicted value of the current block. In this way, the time complexity can be reduced under the condition that the coding and decoding performance is guaranteed.

The application relates to an iterative channel estimation method of a constant envelope modulation system based on DBD decomposition, which comprises the following steps: S1, DBD decomposition is carried out on a constant envelope modulation signal to obtain a decomposition form of the constant envelope modulation signal, and then sampling is carried out on a received signal to obtain a sampling result; S2, an initial estimation value of a channel parameter is obtained; S3, Viterbi demodulation and LDPC decoding are carried out to obtain bit information; S4, a reconstruction signal is generated; S5, the channel parameter is re-estimated to obtain a new channel estimation result; and S6, the new channel estimation result is taken as a current channel estimation result, and S3-S5 are repeated. Compared with the prior art, the application has the advantages of effectively reducing time complexity and space complexity, improving channel estimation performance and the like.

The application discloses a network propagation control method and device based on belief propagation, which comprises the following steps: step one: merging nodes with small influence in the network to simplify the network structure, and obtaining a coarsened network G c (V c ,E c ); step two: identifying key nodes from the coarsened network G c (V c ,E c ) by using an improved belief propagation algorithm (BPD-v), and outputting a propagation control node sequence S; and step three: locally optimizing part of the node sequence in a fine-tuning manner, further improving the effect of the control method, and then obtaining a better control sequence. By the method provided by the application, the resource consumption required for control propagation can be minimized under the premise of meeting the same propagation control effect, or the propagation control effect can be maximized under the premise of the same control resource consumption. In addition, the method has low time complexity and is suitable for super-large-scale network propagation control.

The invention discloses a throughput optimization method in a multi-channel cognitive radio network under AoI constraint. The method comprises the following steps: firstly, constructing a cognitive radio network comprising a main network and a secondary network; secondly, based on the cognitive radio network, defining a state space, an action space and a reward function of the improved soft actor-commentator C-SAC algorithm; constructing a reinforcement learning network model according to a state space, an action space and a reward function of the C-SAC algorithm, deploying the reinforcement learning network model on a receiver, and performing training according to a SAC algorithm mode; and finally, interacting with the environment according to the trained reinforcement learning network model, and performing training in stages to continuously improve the long-term total throughput of the secondary network. According to the invention, the time complexity is extremely low, the long-term throughput is optimized, the freshness of the received data is ensured, and the data mastered by the secondary receiver is prevented from being too old.

The invention relates to the technical field of data storage and management, in particular to an efficient access method and system for time series data. The method comprises the following steps: distributing data to corresponding storage partitions according to business type fields of data records; when the accumulated write-in record number reaches a preset anchor point interval, recording the current timestamp, the file physical offset and the accumulated record number to form an anchor point record; extracting an identifier field value in the anchor point record; matching a hash bucket according to the identifier field value, and writing a hash index entry containing the identifier field value, a timestamp and a file physical offset into the hash bucket according to a time sequence; and positioning the storage partition according to the service type during query. According to the method, an anchor point generation mechanism based on write-in counter driving is introduced into a data write-in link, key position information can be recorded with constant time complexity, and low-overhead write-in process monitoring is achieved.

The embodiment of the invention provides a data query method and device, equipment and a readable medium. The method comprises the following steps: acquiring a query request, wherein the query request indicates at least one entity attribute, an attribute value corresponding to the at least one entity attribute and a logical operation relationship between the at least one entity attribute; based on the at least one entity attribute, the attribute value corresponding to the at least one entity attribute and a plurality of predetermined bitmap tags, at least one target bitmap is determined from the plurality of bitmaps, the bitmap comprises a plurality of data bits arranged in sequence, and the nth data bit indicates that the entity index value is n; and based on the logical operation relationship between the at least one entity attribute and the at least one target bitmap, determining a query result corresponding to the query request. In this way, the response speed of data query can be increased, the time complexity and database pressure are reduced, and storage resources are saved.

This invention discloses a parallel reverse shuffling sampling method, belonging to the fields of graph data processing and machine learning. The method involves sampling k elements from n elements, performing k reverse shuffling operations to process the shuffling process in reverse order, randomly selecting a position within the corresponding deck size range for each operation, and determining the sampling strategy based on whether an element has already been sampled. The time complexity of this method is O(k·log₂k), which is only related to the number of samples, overcoming the limitation of traditional methods by the degree of the original graph nodes. Furthermore, a GPU parallel implementation method is provided, which decouples operation dependencies through a label propagation strategy, achieving a time complexity of O(k·log₂k). 2 (k / warp_size). This invention achieves a 5.89x performance improvement in fanout-based neighbor sampling and a 5.30x performance improvement in probability-based neighbor sampling, and can be widely applied to large-scale graph data processing in fields such as social networks, recommender systems, and bioinformatics.

This invention relates to an engineering optimization method, system, and medium based on the Whale Algorithm (WOA), belonging to the field of intelligent optimization algorithm technology. The method includes the following steps: S1: Initialize parameters and population: Set the population size, maximum number of iterations, dimension, and upper and lower bounds of variables; generate an initial population and initialize the global optimum and the current optimum; S2: Execute individual mutation strategy: Record the position of the global optimum and the position of the current iteration's optimum; S3: Execute a unified search strategy; S4: Execute a group communication strategy; S5: Determine if the current iteration count has reached the maximum number of iterations. If so, output the global optimum; otherwise, increment the current iteration count and return to step S2 to continue iteration. The algorithm proposed in this invention aims to improve the global search capability and convergence accuracy of the algorithm while maintaining a time complexity comparable to the original WOA.

The embodiment of the invention relates to a method, equipment and a computer program product for determining job conflicts. The method comprises the steps of determining a target vertex corresponding to a target job in a dynamic graph based on a job type of the target job; the method further includes generating a target edge associated with the target vertex in the dynamic graph based on the conflict policy identified by the static graph. In addition, the method further comprises the step of determining that the target job conflicts with the running job in response to the fact that the target edge exists in the dynamic graph. Therefore, according to the scheme of the embodiment of the invention, the job conflict detection is modeled as the dynamic graph task, the corresponding edge is generated in the dynamic graph according to the static graph, and the job conflict detection is converted into the detection of the edge in the dynamic graph, so that one-by-one query and comparison of all jobs in the cache are avoided; therefore, the time complexity of conflict detection is reduced, and the operation efficiency of a data protection system is improved.

This invention relates to the field of airport operation management technology, specifically to a method, apparatus, and equipment for real-time detection of aircraft stand conflicts. It constructs a mutual exclusion bitmap matrix based on a pre-acquired list of mutually exclusive edges and bit mapping relationships between stand positions; encodes the current occupancy status of all stand positions into a binary integer occupancy bitmap; traverses the occupancy bitmap from low to high bits, and for each occupied stand i, performs a bitwise AND operation between the mutual exclusion bitmap and the occupancy bitmap to obtain a set of stands mutually exclusive with and occupied by stand i; extracts stand positions with stand numbers greater than i from this set to obtain conflict pairs of stand i, and integrates them into the stand conflict detection result. This solution achieves O(1) time complexity conflict detection by encoding stand occupancy status into a binary bitmap and combining it with a pre-constructed mutual exclusion bitmap matrix. It boasts high detection accuracy and significantly reduced detection time, meeting the requirements of high-concurrency scenarios.

The application discloses an intra-frame fast MTS method based on multi-core transformation and neighborhood coding information from the MTS process of VVC intra-frame coding. First, the correlation between sub-CUs and the relationship between the RD costs of various transformations are utilized to predict the RD cost of the last sub-CU, then whether the current CU needs to be divided is judged according to the relationship between the sum of the RD costs of the sub-CUs and the RD cost of the parent CU, and the MTS candidate list of the current CU is rearranged according to the coding information of the adjacent CUs to further reduce the coding time. The VVC intra-frame fast MTS method disclosed by the application obviously reduces the time complexity of VVC intra-frame coding under the premise that the compression coding efficiency is not obviously reduced compared with the VVC coding standard.

The present disclosure relates to a key generation method and device, electronic equipment and storage medium, the method comprising: obtaining a to-be-processed matrix and an initialization parameter; performing elimination processing on the rth row according to the rth row to the nth row of the to-be-processed matrix of the r-1th processing state and the initialization parameter, to obtain an eliminated rth row; determining whether the rth elimination is successful according to the eliminated rth row; if the rth elimination is successful, performing elimination on the r+1th row to the nth row according to the rth row and the initialization parameter, to obtain the to-be-processed matrix of the rth processing state; determining the to-be-processed matrix of the nth processing state as a target matrix; and generating a key according to the target matrix. The key generation method according to the embodiments of the present disclosure can eliminate the rth row based on the initialization parameter, and eliminate other rows in the case of successful elimination, which can reduce the time complexity, make the running time constant, and improve the operation efficiency.

The application provides a resource scheduling method and system, electronic equipment and a storage medium, and relates to the technical field of computers.The method comprises the following steps: acquiring a coupling relationship graph between nodes and model instances in a resource pool according to the meta information of each node in the resource pool, the deployment information of each model instance in the resource pool, and the production operation characteristics of the resource pool; acquiring a migration relationship matrix between all target model instances on a source node and each target node according to the coupling relationship graph; determining a target optimization algorithm from a plurality of algorithms including a dynamic programming algorithm, a heuristic search algorithm and a greedy algorithm according to a time complexity growth factor corresponding to the migration relationship matrix; generating a target resource scheduling scheme according to the target optimization algorithm, and migrating each target model instance from the source node to each target node according to the target resource scheduling scheme.The application effectively improves resource utilization while ensuring the stability and operation efficiency of cluster services.

The invention belongs to the technical field of fault diagnosis, and particularly relates to a model diagnosis system coding reduction method and device based on component regulation and a medium. The method comprises the following steps: defining a governing component, a governed component, a governing core and top layer diagnosis, identifying each component in a circuit, defining a front component and a rear component, identifying the front component and the rear component of the identified governed component, and formulating a hard clause coding rule of the governed component for reduction; the method comprises the following steps: identifying components, defining related variables and formulating a rule for performing reduction in combination with the related variables, performing component reduction on the identified components through output, performing data observation and formulating a replacement rule for governing the components and a replacement rule for governed components, and performing hard clause and soft clause coding on the reduced circuit according to observation data and output. And finally, combining the time complexity of the compression model with a MaxSAT solver to obtain a candidate diagnosis result.

The invention provides a cache cleaning method, device and equipment, is applied to the technical field of a distributed system and a distributed system, and aims to reduce the time complexity of cache cleaning, the method comprises the following steps: initializing a time wheel, the time wheel comprises a plurality of slot positions, and the number of the slot positions corresponds to the cycle time of the time wheel; obtaining expiration time of the multiple pieces of cache data, and obtaining a current timestamp of a slot position to which a time pointer of the time wheel runs; based on the expiration time and the current timestamp, the multiple pieces of cache data are stored in corresponding slot positions respectively; and the time wheel is operated, so that when the time pointer of the time wheel is operated to each slot position of the time wheel, the cache data stored in the slot position is cleared.

The present invention is applicable to the technical field of wireless communications, and particularly relates to a microwave network connection method and system, and a related device. The method of the present invention comprises: on the basis of whether port attributes are equipotential, dividing a first sparse block diagonal matrix into four sub-matrices, so as to obtain a second sparse block diagonal matrix comprising the four sub-matrices; acquiring a transfer matrix used for representing power distribution of the equipotential ports of microwave networks to be connected; and, on the basis of a preset rule, performing computation on the transfer matrix and the second sparse block diagonal matrix, so as to obtain a scattering parameter matrix; and, by means of the scattering parameter matrix, connecting the plurality of microwave networks to be connected. Compared with the prior art, the present invention involves dividing a sparse block diagonal matrix into four smaller sub-matrices, which have lower computation time complexity, thus significantly reducing computation time and improving computational efficiency during computation, thereby improving the efficiency of microwave network connection.

The invention relates to the field of video signal processing, and particularly discloses a real-time video compression system for multi-core parallel processing. The system comprises a video frame analysis module, a region segmentation module, a priority allocation module, a task dependency graph construction module, a task scheduling module and a resource allocation module. The core of the method is that a space-time complexity mapping graph is generated by analyzing texture and motion features of video frames, and compression areas are dynamically divided and processing priorities are allocated according to the space-time complexity mapping graph; and then a priority-based dynamic task dependency graph is established, tasks are allocated to multiple processing cores by adopting a critical path priority scheduling strategy, and differentiated computing resources are allocated according to the priority. Through content-aware adaptive task division and scheduling, the problem of low efficiency caused by uneven load in a multi-core system is effectively solved, and the processing efficiency of real-time video compression and the utilization rate of system resources are improved.

The application discloses a feature subset selection method and device and a storage medium. The method comprises the following steps: acquiring high-dimensional feature data; obtaining a feature importance list by using a random forest; obtaining a feature cross-correlation matrix by using Spearman correlation; obtaining a fitting degree of each feature according to a target GRU model; performing feature relationship type fusion on the feature importance list, the feature cross-correlation matrix and the fitting degree of each feature to obtain an overall evaluation value of each feature; continuously performing feature sorting on the overall evaluation value of each feature, removing the feature with the lowest evaluation value and determining whether the number of the remaining features is less than a required number of features until the number of the remaining features is not greater than the required number of features; and obtaining a feature subset. The feature subset selection method of the application uses Spearman correlation analysis, a random forest and a GRU for joint evaluation, completes diversified feature evaluation, realizes feature data dimension reduction, improves the feature evaluation processing flow and reduces the time complexity and the space complexity for subsequent work.

The invention discloses an automatic operator selection method for flexible production line scheduling and process parameter optimization, which takes machine layout configuration, buffer bins, part batches, arrival time and scheduling strategies as design variables, each design variable has a respective dimension according to the scale of a production line, and comprises the following steps: constructing a bottom optimizer operator selection pool, constructing a high-dimensional optimization problem, and grouping variable dimensions of the high-dimensional optimization problem; each group is optimized in sequence in each iteration; extracting features, and selecting an underlying optimizer operator by using a deep reinforcement learning model; and calculating an award of the deep reinforcement learning model, and updating the deep reinforcement learning model. According to the method, cooperation and co-evolution group-by-group optimization is utilized, so that better optimization performance can be obtained, the time complexity of operator optimization can be reduced, and the rationality and efficiency of a whole production scheduling scheme can be improved while local scheduling and parameter optimization quality are ensured.

The application discloses a parallel hypergraph construction method and device based on FPGA. The device comprises a computer system and an FPGA which are connected in communication. The computer system is configured to send an undirected adjacency matrix representing a common graph to the FPGA, receive all hyperedges sent by the FPGA and construct a hypergraph according to all hyperedges. The FPGA is configured to receive and store the undirected adjacency matrix, generate an adjacency vertex set corresponding to each target vertex according to the undirected adjacency matrix, construct a hyperedge corresponding to each target vertex in parallel according to the adjacency vertex set, and transmit all hyperedges to the computer system. The device and method convert the time complexity of hyperedge construction into spatial complexity through parallel computation of FPGA hyperedges, thereby reducing the operation time overhead and realizing fast construction of an accurate hypergraph.