6results about How to "Avoid information loss" patented technology

The invention relates to the technical field of metal button surface defect real-time detection methods, in particular to a metal button surface defect real-time detection method based on dry deep learning, which comprises the following steps: acquiring a to-be-detected metal button image and preprocessing the to-be-detected metal button image to obtain a to-be-detected metal button image; and inputting the preprocessed image into a deep learning network model to extract a defect region, optimizing and verifying the extracted defect region, and finally generating a statistical result of defect detection. According to the method, dynamically regulated progressive boundary construction, adaptive gradient mapping, Gaussian fitting detail enhancement, multi-dimensional difference correction factors, sectional defect region generation and other algorithms are innovatively introduced in sequence, so that boundary recognition and detail optimization are progressive layer by layer. The innovative multi-stage optimization design enables the data output of each step to be connected with the subsequent steps, guarantees the continuity and consistency of defect detection, and avoids the problems of boundary error and information loss caused by algorithm independence in a traditional method.

The invention belongs to the field of log anomaly detection of a distributed system, and provides a log anomaly detection method based on Transform and time convolution, and the method comprises the following steps: S1, obtaining log data in a real environment; s2, log preprocessing: preprocessing the obtained log data set to obtain first log content; s3, dividing the log sequence: selecting a proper division strategy for the features of the first log content to obtain a second log content; s4, performing vectorization processing on the second log content to obtain a log vector fused with time, risk types and semantic features; s5, constructing a dual-channel anomaly detection model; and S6, traversing the log data set to train a model, and inputting the log vector into the trained anomaly detection model to perform log anomaly detection. According to the method, the problems of poor generalization ability and information loss in traditional log analysis are avoided, and the accuracy and efficiency of log anomaly detection are improved.

The invention relates to a transaction response state processing method based on RPC (Remote Procedure Call) exception return, which comprises the following steps that: a service party defines a specific transaction response code, and packages the transaction response code into an exception class object to be thrown out according to a transaction response state corresponding to an exception class when a transaction error occurs; ensuring that the transaction response code is consistent with the transaction response state corresponding to the abnormal class; the consumer judges a transaction response state according to the abnormal class object after receiving the abnormal class object thrown by the service party; when the consumer cannot process the abnormal class object, the abnormal class object is thrown to an upstream system, and it is ensured that the abnormal class object is kept consistent with the transaction response state of the response code in the transmission process; and the upstream consumer repeats the second step until a certain system receives the abnormal class object, and judges the transaction response state according to the abnormal class object to carry out correct error processing. According to the method, information loss and transaction processing risks caused by multiple times of transmission of the transaction code in a traditional method are avoided, the cost of global management of the response code is reduced, and the limitation on transaction response processing of the system is reduced.

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention discloses a map matching and forward and reverse projection wide remote sensing image segmentation and sample automatic generation method. The method comprises the following steps: step 1, taking an original image, metadata and coastline vector data as an input triple; data cleaning and normalization are carried out through an image preprocessing module; entering a geometric correction and registration module to generate a corrected image with an Alpha channel; 2, performing spatial indexing and matching on the coastline vector data and the corrected image; using a mask generation algorithm to automatically align and rasterize the vector data, and constructing a final true value mask of pixel-level alignment; 3, initializing a reverse mapping grid through a coordinate transformation processing module; calculating a nonlinear mapping relation from geographic coordinates to original sensor pixel coordinates by using a coordinate inverse transformation calculation module; and through resampling processing, accurately backfilling the semantic tag to an original image space, and outputting a final inverse transformation mask. According to the invention, high precision is ensured, and meanwhile, the order of magnitude of processing speed is improved.

The invention provides a cross-modal fusion time sequence prediction method, device and equipment based on a large language model, and relates to the technical field of time sequence prediction. The method comprises the following steps: acquiring numerical time sequence data in a historical time window and associated text information; respectively processing the numerical time sequence data and the text information through parallel processing modal preprocessing branches to obtain a time sequence feature representation, a semantic reasoning prediction result generated by a large language model and a text semantic feature representation; performing hierarchical interactive fusion on the time sequence feature representation, the semantic reasoning prediction result and the text semantic feature representation through a multi-stage cross-modal attention fusion network to obtain enhanced time sequence representation; generating an adaptive fusion weight for each time step in the prediction time window based on the enhanced time sequence representation; and carrying out weighted fusion on a numerical prediction result generated based on the time sequence feature representation and a semantic reasoning prediction result by using the adaptive fusion weight to obtain a final time sequence prediction result.