258 results about "Time complexity" patented technology

The embodiment of the invention discloses an instruction translation method and device, electronic equipment and a storage medium, and the method comprises the steps: receiving an instruction translation request, when the instruction translation request carries a target address of an original instruction, obtaining the target address in the instruction translation request, obtaining a first symbol table file, retrieving a target node in the tree data structure of the first symbol table file according to the target address, and reading symbol offset from the target node; retrieving a function number of the target address and a first line number of the original instruction from first symbol information of the first symbol table file according to the symbol offset; the first symbol dictionary is obtained according to the target address, the first function character of the original instruction is retrieved in the first symbol dictionary according to the function number, and the translation result of the original instruction is obtained based on the first function character and the first line number, so that the time complexity during instruction translation can be reduced, and the instruction translation efficiency is improved.

The invention discloses a low-orbit super-dense constellation optimization method based on an NSGA-II algorithm, and belongs to the field of satellite communication. According to the method, the problems that in the scene of low-orbit super-dense constellation configuration optimization, an existing optimization method is high in time complexity, the constellation coverage performance cannot be guaranteed, and the inter-satellite link richness requirement cannot be met are solved. The method comprises the following steps: firstly, simulating a constellation, and establishing a target function according to a constellation optimization target; establishing a multi-objective optimization model by taking parameters of the constellation as input and taking the established objective function value as output; then, an NSGA-II algorithm is adopted to solve the established multi-objective optimization model, and a pareto leading edge point set and an optimal solution set are obtained; and finally, K pareto front edge points are selected from the obtained pareto front edge point set, and low-orbit super-dense constellation design is carried out based on constellation parameters corresponding to the selected pareto front edge points in the optimal solution set. The method can be applied to low-orbit super-dense constellation optimization.

The invention discloses a multi-user short-term power load prediction method for an improved Transform model, and relates to the technical field of novel power systems, and the method comprises the steps: S1, setting an internal decomposition block based on a moving average and Fourier transform thought, integrating the internal decomposition block into a frame, and decomposing an input data sequence into trend, season, holiday and festival variables and residual variables; s2, an Nystrom method is used for approximating a softmax matrix in a standard self-attention mechanism, and the softmax matrix is used for coping with challenges of secondary time complexity and memory use in Transform; s3, performing deep feature mining on the seasonal part after time sequence decomposition by using convolution operation; and S4, probability prediction based on Monte Carlo random inactivation. And designing an internal decomposition module of the depth model, and extracting an internal complex time sequence trend of a hidden state in the model by the module, so that the model has a progressive decomposition capability of a complex time sequence. Different from a traditional load prediction method for exploring time characteristics of a single user, a space-time attention mechanism is improved, and accurate multi-user short-term load prediction is provided.

The invention relates to the technical field of short-term load prediction in a power system, and discloses a short-term load prediction system based on a coder-decoder architecture, and the system is characterized in that a coder is used for extracting local features of a power load mode; and the decoder is used for converting the local features of the power load mode into predicted power load values and outputting the predicted power load values. The encoder is realized by a multi-scale expansion causal convolutional network MSDCC, and the decoder is realized by a bidirectional long short-term memory network BiLSTM. The prediction system construction method comprises the following steps: extracting related data from a historical database, preprocessing and analyzing the data, and constructing a predictor matrix; an MSDCC encoder is constructed; a BiLSTM decoder is constructed; and combining the encoder-decoder architecture to construct a short-term load prediction model. According to the method, the size of the feature map is effectively limited, model parameters are reduced, overfitting is avoided, calculation requirements are controlled, non-linear features are efficiently captured, meanwhile, time keeping complexity is low, and therefore the method has the advantages of being high in prediction efficiency and high in prediction precision.

The invention provides a time sequence clustering method and device, and the method comprises the steps: obtaining subsequences of a to-be-clustered time sequence, taking m subsequences as m clustering centers, and combining the m clustering centers into a clustering center set of the to-be-clustered time sequence; a target function is constructed with the purpose of maximizing the sum of the shape similarities of all the subsequences and the corresponding clustering centers, and one subsequence corresponds to one clustering center; and performing approximate solution on the target function based on a greedy strategy, and accelerating the solution process through a preset effective boundary to determine an approximate optimal subsequence combination in the clustering center set. According to the invention, the time complexity is significantly reduced by selecting the subsequences as the clustering center; a greedy strategy is introduced to enable a suboptimal solution to quickly approach a globally optimal solution, the solving process is accelerated by setting an effective boundary, and the clustering efficiency of a long-time sequence is effectively improved.

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 UAV and USV network resource allocation system and method based on an improved whale algorithm. A constraint condition construction module is used for taking a correlation coefficient between a USV and a UAV, a service duration of providing service for the UAV by the UAV and a processor calculation frequency of the USV as constraint conditions; the optimization problem construction module is used for constructing optimization problems of minimizing USV task processing energy consumption and maximizing USV data processing amount; and the resource allocation module is used for iteratively solving the optimization problem by using the improved whale algorithm to obtain resource allocation between the UAV and the USV network. According to the method, a traditional whale optimization algorithm is fused with a novel coding mode and crossover mutation operation, the problems of poor feasible solution performance, poor convergence and high time complexity in existing resource allocation are solved, and the QoS requirement of intensive tasks is met.

The invention relates to the technical field of data processing, and discloses a space-time K function graph generation method and system based on distribution and aggregation, a terminal and a storage medium, the method comprises the following steps: obtaining a position data set of a target area, constructing a first matrix, performing data point access on the position data set to obtain an access result, and performing range query according to the access result to obtain a space-time K function graph; obtaining a first range query set; constructing a second matrix, and performing element allocation on the second matrix according to the first range query set to obtain a target second matrix; constructing a third matrix, performing numerical calculation on all elements in the third matrix to obtain a target calculation result, and performing optimization processing on the first matrix according to the target calculation result to obtain a target first matrix; and generating a space-time K function graph according to the target first matrix and the space-time K function minimum value and the space-time K function maximum value of the randomly generated data set. According to the method, the time complexity of generating the space-time K function diagram is reduced while the precise solution and the same space complexity are kept.

The invention discloses a method for predicting the residual service life of equipment based on an improved Mama framework. The method comprises the following steps: step 1, preprocessing multi-dimensional data; step 2, a Mama model is improved; and 3, predicting the remaining service life. According to the method, the unique selective state space mechanism of Mamba is fully utilized, so that the time complexity is controlled to be O (N) when long-sequence data is processed, and the calculation cost which is increased along with the square level of the sequence length in a traditional model is avoided. And meanwhile, an intelligent scaling regulator is constructed in Residual Block of the Mama framework so as to dynamically adjust the weight of residual error connection. A plurality of attention branches which are operated in parallel are creatively integrated in MambaBlock and act on data processed by a convolutional layer, so that the capturing capability of a model on a complex data relationship is enhanced, and the prediction precision is improved.

The invention discloses a long-term wind power prediction method based on improved Bert. The method mainly comprises the following steps of: firstly, preprocessing an abnormal value and time information of fan information in the data concentration; thirdly, processing the data by using a full connection layer and a convolutional layer to obtain a high-dimensional representation of fan information with time sequence information and time information; a full connection layer is used for processing the multi-head self-attention mechanism, and a shared query key matrix and a value matrix needed by the multi-head self-attention mechanism are obtained respectively. Then, a random rotation method is used to map the data matrix into different hash buckets, and a multi-head self-attention mechanism is used in each hash bucket. And finally, obtaining a final result by using residual connection and layer normalization processing. According to the improved Bert model provided by the invention, the prediction accuracy can be improved while the time complexity is reduced to O (nlogn).

The invention discloses a multi-source time sequence test data time alignment method and a storage medium, and the method comprises the steps: obtaining the data of each product in a test, and calculating the maximum data collection frequency and time sequence data reference time axis of all products; performing preliminary time alignment on each product; static products and dynamic products are distinguished, and different types of products are subjected to time alignment according to the time sequence data reference time axis in different modes. According to the method, the unique reference time axis is calculated by calculating the maximum data acquisition frequency, the time complexity is O (1) when time matching is carried out on data at each moment, and time sequence data time preliminary alignment work can be completed at a higher speed when the data size is too large. According to the invention, for the time sequence data which is subsequently added into time alignment calculation, the current time sequence data which is subjected to time alignment cannot be influenced. Meanwhile, data preprocessing work is carried out according to the data features of different data sources, so that the time alignment work of the multi-source time sequence data is more complete.

The invention provides a time-varying parameter model identification method based on beta wavelet basis function expansion. As shown in the flow in figure 1, the method comprises the following steps: firstly, establishing a nonlinear autoregression time-varying parameter model by using a non-stationary time-varying signal, and determining the order of the model by using an FPE order determination criterion; performing linear weighted expansion on the model time-varying parameters by utilizing a first-order beta wavelet basis function, and converting a model time-varying parameter identification problem into a time-invariant parameter estimation and model effective item selection problem, namely obtaining a beta wavelet expansion time-invariant parameter model; selecting effective items of the model by adopting a classical orthogonal forward regression algorithm, determining the number of the effective items of the model in combination with an APRESS cross validation standard, eliminating redundant items in the expansion model, estimating corresponding time-invariant parameters of the model, and establishing a sparse model structure; and finally, in combination with the obtained effective items and corresponding parameters, obtaining a sparse nonlinear autoregression model and time-varying parameters thereof through reverse solution. Compared with an existing time-varying parameter identification method based on main B-spline multi-wavelet basis function expansion, the time complexity of the identification process can be effectively reduced, the identification precision of a sparse model is improved, and a new thought and a new calculation framework are provided for identification analysis of nonlinear time-varying parameter signals.

Task assignment for multi-robot systems (MRSs) in a smart factory (Industry 4.0) is described. Aspects are directed to a hypergraph based MRS and production model facilitating the cooperation among robots and serving frequent reconfiguration desired in Industry 4.0. Aspects are directed to a time complexity friendly search algorithm for real-time application using a hypergraph model to get task assignment(s). Parameters are provided for a tradeoff between solution optimality and time complexity. In an implementation, an example system can include a MRS including robots, wherein the MRS is configured to perform a manufacturing task, and a computing device configure to perform a multi-robot task allocation (MRTA) for the MRS. In an implementation, an example method can include generating task assignments, using MRTA, for robots of a MRS including the robots, wherein the MRS is configured to perform a manufacturing task, and providing the task assignments to the MRS.

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 discloses a finite-state machine coverage rate statistical method of a VCD (Video Compact Disc) file, which is characterized in that a mapping relationship is established between a signal name and state transition information by directly analyzing a YAML (Young Attribute Markup Language) format file of finite states and transition behaviors among the states compiled in a design stage and adopting a hash table mapping technology, so that the constant-level efficiency of reducing time complexity to O (1) is realized. When the VCD file generated by simulation is processed, two strategic file reading methods are adopted, and the file processing flow is simplified. And the uniqueness and accuracy of data processing are ensured through a self-defined Hash algorithm. In simulation data processing, screening is carried out according to the rule of the first character of the data, and only key state transition information is reserved. According to the method, the defects of processing the VCD file and counting the coverage rate of the finite-state machine under the condition of limited resources in the prior art can be effectively overcome, the counting speed of the coverage rate of the finite-state machine is increased, the efficiency of the verification process is improved, and remarkable performance improvement is brought to the verification stage of integrated circuit design.

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 invention discloses an improved two-dimensional ordered statistical constant false alarm rate detector implementation method based on a CPU. The implementation steps are as follows: inputting a two-dimensional data matrix; performing initialization setting; for each detection unit, judging whether the sample value of the detection unit is the maximum value in a nine-block grid taking the detection unit as the center or not before detection; if yes, judging whether the sample value of the detection unit is greater than k arbitrary values of all reference unit sample values multiplied by a threshold factor or not through parallel processing; if yes, the detection unit is judged as a target, and the adjacent undetected unit does not need to be detected subsequently. According to the method, the number of detection units is greatly reduced, the processing time is shortened, meanwhile, in the detection process, a CPU does not need to open up an extra memory space to store all reference units to be sorted, data sorting work which is complex in traditional two-dimensional ordered statistics constant false alarm rate and long in consumed time is ingeniously avoided, the time complexity and the space complexity of an algorithm are reduced, and the detection efficiency is improved. And the real-time performance of target detection is ensured.

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 method for inversely sorting and assembling tree data, which relates to the field of software background development and comprises the following steps: S1, preparing; s2, a sorting step; s3, a cache establishment step; and S4, traversing and assembling. Compared with a traditional assembling mode of arranging from root nodes in sequence and recurring layer by layer, the method for assembling the tree structure data in the inverted sorting mode has the remarkable beneficial effects that the basic data are traversed in the inverted sorting mode, calculation is started from the last-stage node, the lower-stage nodes are set, and the efficiency is improved. According to the method, the complete tree structure can be assembled only through one-time circulation, the number of times and depth of recursion calling are greatly reduced, the time complexity problem (O (1)-O (n2)) caused by too deep recursion hierarchy and uncertain tree branch is avoided, and therefore the response time of a tree interface is remarkably shortened, and the overall running smoothness of software is improved.

The invention discloses an optimal impossible Boomerang attack search method based on MILP, and the method aims at a block code algorithm of a 4-bit s box, builds a bit-level differential propagation constraint description for linear and nonlinear components of the block code algorithm, employs a contradiction point in a BCT table as a judgment method, can search an impossible Boomerang discriminator of a plurality of different contradiction points under the same round number, and achieves the optimal search of the optimal impossible Boomerang attack. A key recovery expansion round is further added into the model, plaintext difference and ciphertext difference are taken as objective functions, a complete MILP model impossible to Boomerang attack is constructed, and the minimum number of plaintext and ciphertext active bits can be obtained by solving the model, so that the overall time complexity is reduced, and the probability of the key recovery expansion round is reduced. The method is suitable for searching high-round impossible Boomerang attacks under the condition of related keys or related adjustability.

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 present disclosure provides a method, device, electronic device and storage medium for checking the repeatability of seismic data, the method comprising obtaining a record file of seismic exploration auxiliary data in a target area; preprocessing the record file to read each record in the record file and converting each record into a one-dimensional array indexed by the row number of each record in the record file; calculating the hash value of each record by the MD5 information digest algorithm; inserting the array corresponding to each record into a balanced binary tree in turn to obtain duplicate records and their row numbers in the record file; wherein the key value of each node of the balanced binary tree is the hash value of the corresponding record; and outputting the duplicate records and their row numbers in the record file. The method is based on a balanced binary tree and realizes rapid duplication checking of seismic exploration auxiliary data records, with a time complexity of up to O(log n), significantly improving the SPS file processing efficiency and reducing the time cost at the construction site.

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 provides a dynamic network capacity calculation method and device based on topology change characteristics, and belongs to the technical field of communication. With the maximum hop count and the maximum spanning time slice number as constraints, augmented path search is carried out on the residual network constructed based on the time extension graph in a breadth-first search mode, that is, the hop count of the search path and the spanning time slice number thereof are concerned all the time; according to the method, the search paths exceeding the set maximum hop count and the set maximum spanning time slice number are pruned, that is, some overlong search paths are removed, so that the range needing to be searched is reduced, the time complexity of searching the augmented path is reduced, and when the augmented path is searched, the search is preferentially performed in the same time slice, so that the search efficiency is improved. And it is ensured that the target augmented path with the minimum number of spanning time slices can be searched preferentially.

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.

The invention discloses a scheduling table generation method and system for a time-triggered service, and mainly solves the problem of high time solving complexity in a large-scale scene in the prior art. According to the implementation scheme, a TTE scheduling model and a service conflict graph are constructed, and a maximum connected subgraph is obtained; independently dividing each sub-graph into sets, dividing services into link-conflict-free groups in sequence in each set, and distributing the groups to free time slots in a time axis; judging whether a group of which the time slot is not successfully distributed exists in the current maximum connected sub-graph; if so, trying to distribute again through backtracking operation; if not, obtaining the sending moment of the service at the source end according to the time slot where the group of the service is located and the intra-group time slot offset of the service; and calculating the sending time and the receiving time of each switch and the receiving time of the destination end according to the sending time of the source end, and generating a conflict-free scheduling table. According to the method, the time complexity of scheduling solution is reduced, and the method can be flexibly applied to scheduling planning in various scenes such as unicast service, multicast service, redundant service and mixed service.

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 invention discloses a k-center problem solving method using small-batch data updating, and relates to the technical field of data updating, and the key points of the technical scheme are as follows: the method randomly selects k points from a full set to initialize a central point set, then divides the remaining points in the full set into a plurality of batches, and only uses one batch of points to update the central point set every time, and a final center point set is obtained after updating is completed. The method has the advantages of low time complexity and small occupied computing resources.