7 results about "Cycle detection" patented technology

The invention relates to the technical field of power system data processing, in particular to a power system data processing method and system based on dynamic period detection, and the method comprises the steps: collecting a historical query log of a power business system, and extracting time sequence data of query features through a sliding window; training a recurrent neural network model by using the time sequence data, and predicting a current optimal query period length; on the basis of the optimal query period length, predicting an index combination required by query through a classification model, and generating a candidate index set; optimizing an execution strategy of the candidate index set by taking the minimum index construction cost and the maximum query coverage rate as targets; and obtaining query data of the power business system in real time, and updating the candidate index set and the execution strategy when the change of the business period characteristic is detected. The objective of the invention is to match the periodic change of the power system data, improve the indexing efficiency and reduce the maintenance cost.

The application relates to the technical field of rotating machinery equipment fault diagnosis, and specifically discloses a vibration signal impact cycle detection method, device and equipment, which comprises the following steps: firstly, performing windowing processing on the vibration signal of the rotating machinery equipment, and extracting an upper envelope equal in length to the windowed signal; secondly, determining the estimated impact cycle of the windowed signal through an unbiased autocorrelation function and a biased autocorrelation function; then, obtaining the impact sequence from the upper envelope of the windowed signal through morphological top-hat transformation, and extracting a first principal feature component from the extended feature sequence by using a PCA algorithm to obtain an abnormal score sequence, so as to determine the impact index of the windowed signal by using a clustering algorithm; finally, determining whether the period of the impact in the windowed signal is found by judging whether the ratio of the maximum length of the arithmetic sequence in the impact index of the windowed signal to the estimated impact number exceeds a specified predetermined proportion threshold.

The invention discloses a micro-service resource management method and device, a medium and a program product, and the method comprises the steps: obtaining historical time series data of a micro-service associated resource, and carrying out the prediction of the historical time series data through employing a robust period detection algorithm, and obtaining a single-period prediction result; decomposing the historical time sequence data by adopting a robust time sequence analysis algorithm to obtain a trend component sequence, a periodic component sequence and a residual component sequence; inputting the residual component sequence into a long sequence deep learning network, and outputting a residual prediction result through the long sequence deep learning network; and determining a resource prediction result corresponding to the micro-service according to the single-cycle prediction result, the trend component sequence, the periodic component sequence and the residual prediction result, and managing resources associated with the micro-service according to the resource prediction result. According to the technical scheme of the embodiment of the invention, the precision of a micro-service resource prediction result can be improved, and a reliable basis is provided for an elastic scaling decision.

The application provides a method and device for detecting a dead loop, which can solve the problem of incorrect identification of a dead loop, thereby improving the detection accuracy of the dead loop, and can be applied to a terminal device or a network device. The method comprises the following steps: determining an intermediate representation of an application program, the intermediate representation being determined according to source code and / or an installation package of the application program, the intermediate representation comprising a linear intermediate representation and a graph intermediate representation; obtaining a loop body according to the intermediate representation; and determining that the loop body has a risk of causing a dead loop.

The invention belongs to the technical field of large models, and particularly relates to an SQL (Structured Query Language) conversion method, system and equipment based on a large model and a storage medium. The technical problem that in the prior art, errors in SQL query statements are difficult to effectively verify and repair, and consequently the SQL conversion and generation accuracy of related technologies is low is solved. Comprising the following steps: analyzing a natural language of a user, identifying and mapping SQL skeleton components to a predefined Python action function, and generating an initial action sequence; and then, the sequence is verified through a detection tool, if an error is found, error information is fed back to the large model to correct the sequence, detection is cyclically carried out until the sequence passes or reaches the maximum number of iterations, and a final action sequence is output. And if no error exists, directly outputting. And finally, switching the final sequence to an SQL splicing mode, executing each action function to obtain SQL clause fragments, and performing character string splicing to generate a complete SQL query statement.

A method, system, and computer program product for reducing memory consumption in a depth-first search (DFS)-based cycle detection process for detecting n-length cycles on a Pregel model is provided. The method includes generating a graph comprising a plurality of vertices and edges connecting the plurality of vertices, starting a cycle from a first vertex in the plurality of vertices, setting an ID of the first vertex to a path and the first vertex as a target vertex, and iterating the following n-1 times: sending the path to an outgoing edge from the first vertex, and transmitting the path n-1 times by a subsequently received vertex via the outgoing edge, finding a vertex before returning to the target vertex, adding an ID of the found vertex to the path, and setting the found vertex as the target vertex, such that n = n-1, where n is a number of iterations.

The invention provides a mechanical arm dynamic time domain target tracking method based on a diffusion model, relates to the technical field of mechanical arms, and solves the problems of insufficient rapid adaptability and real-time responsiveness of a diffusion model in a dynamic environment through cyclic detection feedback and real-time planning by adopting a dynamic time domain and diffusion model combination strategy. Meanwhile, when the two are combined, a diffusion model cannot predict and plan an action sequence range, and forced target tracking leads to multi-modal weakening and mode collapse, a target tracking judger is designed, target tracking is introduced in a proper state, the diversity of solutions and the accuracy of target tracking are balanced, and the target tracking accuracy is improved. Therefore, real-time dynamic environment perception and expected action generation of the mechanical arm are achieved, and the task execution precision is improved.