16 results about "Occurrence data" patented technology

This invention discloses a deep learning-based method for predicting the phenological synchronization of specialized butterflies and their host plants, belonging to the interdisciplinary field of biodiversity conservation, ecological prediction, and deep learning technology. The method includes: collecting environmental climate variables, butterfly occurrence data, and phenological time-series data of their host plants; preprocessing the data to create training and validation sets; constructing a dual-label system with the day of year (DOY) of butterfly occurrence peaks (e.g., emergence peak, egg-laying peak) as the primary label and the normalized value of synchronization deviation as the secondary label; building a deep learning model with ecological weights, including an embedding layer, an LSTM layer, and a fusion layer; inputting a vector concatenated from daily or weekly climate time-series features and ecological dynamic features; and outputting the primary and secondary labels through a dual-output layer; designing a hybrid loss function that integrates the main regression loss, ecological synchronization constraint loss, and small-sample regularization loss; training the model using the Adam optimizer and implementing an early stopping mechanism; and validating and evaluating the model using regression accuracy and ecological synchronization accuracy indicators. The method includes modules for data acquisition, preprocessing, label construction, model training, prediction evaluation, and storage. This invention improves the accuracy of phenological synchronous prediction by embedding ecological dynamic feature weights and dual-label constraints, adapts to small sample scenarios, and provides technical support for the monitoring and protection management of rare butterfly species (such as the Golden Birdwing and the Chinese Tiger Swallowtail).

Embodiments of the present application disclose a data processing method, device, equipment, medium and program product. The method comprises: in the case where it is detected that a second window has data update, acquiring first data information sent by the second window, the first data information comprising data updated by the second window; comparing the first data information with subscription data information of each subscription information in a plurality of subscription information set in advance to determine target subscription information, the subscription information comprising subscription data information and identification information of a subscription object, the subscription object comprising a subscription component and / or a subscription page; determining a target subscription object according to the identification information of the subscription object in the target subscription information; and sending the first data information to the target subscription object for data update by the target subscription object according to the first data information. The embodiments of the present application can reduce the risk of page lagging under the premise of realizing data synchronization between windows, thereby improving user experience.

Systems, computer-implemented methods, and computer program products to facilitate modeling bi-directional effects between events and system variables are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components comprise a machine learning component that learns mutual dependencies jointly over event occurrence data and transition data, wherein the transition data comprises state variable transitions observed over a multivariate state variable set.

The application belongs to the technical field of pattern matching and mining under the hypergraph data model, and particularly relates to an incremental dynamic hypergraph matching method centered on overlap, which comprises: on the basis of OHMiner, when the data of a pattern hypergraph changes, partial updating is performed on OIG; when a hyperedge is inserted, new overlap relationships brought by the new hyperedge are calculated, and the new overlap relationships are added to OIG as new nodes in an incremental manner, and the same nodes are merged; when the data of a target hypergraph changes dynamically, DDAL is updated incrementally, and pattern matching is performed in an incremental manner; when a new hyperedge is added, the existing partial matching affected by the new hyperedge is searched in a partial matching state storage library, each partial matching instance is incrementally expanded, and the size of a new overlap is calculated; the size of the new overlap is compared with a requirement in an execution plan to verify, and invalid candidates are discarded, which is incremental pruning. The application introduces a brand-new computing paradigm for dynamic hypergraph matching, and optimizes the performance of dynamic pattern matching.

The application relates to the field of intelligent Internet of Things and equipment management systems, and discloses an experimental equipment management system associated with an Internet of Things RFID, which comprises a collection module for collecting source data; the source data comprises spatial attribute data, function association data, equipment association data and equipment co-occurrence data of experimental equipment in an experimental equipment set deployed in a laboratory; a graph construction module for constructing a dynamic heterogeneous graph based on the source data; a fusion module for performing spatio-temporal fusion on data respectively carried by spatial mutual exclusion edges, function cooperation edges, equipment proximity edges and equipment co-occurrence edges in the dynamic heterogeneous graph to obtain a fusion result; and an equipment management module for determining an expected equipment state of the experimental equipment set in at least one time period based on the fusion result and managing at least part of the experimental equipment set based on the expected equipment state. The scheme can meet diversified experimental equipment management requirements.

A method, apparatus, equipment, and computer-readable storage medium for automatic post-earthquake inspection of hydropower stations include: extracting target data within a pre-set time period before an earthquake occurs; comparing the collected post-earthquake key data with the target data to determine whether data anomalies occur, wherein the comparison includes deviation thresholds, trend slopes, or multi-parameter correlations; if data anomalies are determined to occur, issuing an early warning and displaying the abnormal data; and performing in-depth analysis on the abnormal data to address the technical problem of extremely low inspection efficiency in existing manual post-earthquake inspection methods, thereby improving inspection efficiency.

Provided is a convolutional neural network structure based on channel-specific convolution for high-speed object recognition of a 3D LiDAR, including: an inside-channel convolutional network for extracting features in channels of a LiDAR data; an outside-channel convolutional network for extracting features between the channels by using outputs for the channels of the inside-channel convolutional network and generating a feature map representing the extracted features between the channels; and a detection network searching location and class of an object by using the feature map generated from the outside-channel convolutional network. According to the invention, since a raw data coming from a LiDAR is directly used in a high-speed object recognition procedure of a 3D LiDAR, there is an effect that data loss does not occur.

An information processing system according to the present invention is made up of a plurality of participating nodes and performs federated learning on the basis of sequentially obtained local data. The information processing system includes a detecting means that detects a data abnormality in at least one of the plurality of participating nodes, and a control means that determines which of a plurality of countermeasures is to be implemented when a data abnormality occurs.

The method, device and system for initializing a VIO system relate to the technical field of visual positioning and navigation, and the method comprises the following steps: obtaining state information of the VIO system; sending indication information to a control device of a self-moving device according to the state information, the indication information being used for instructing the control device to control the self-moving device to first be stationary and then be started; and when it is determined that the self-moving device has been stationary, performing a static initialization operation on the VIO system to determine an initial value of the VIO system, the state information comprising at least one of the following: the VIO system initiating a first signal, the first signal being used for indicating that the VIO system is about to be initially started, or data abnormality occurring in a data processing process of the VIO system, or a preset time duration being experienced since a time point at which a last static initialization operation is completed. Based on the scheme, the VIO system can be provided with a condition for static initialization, so as to implement a static initialization operation on the VIO system, and the efficiency of initialization can be improved.

This invention discloses a dynamic orchestration method and related equipment based on field-level declarative dependencies, relating to the field of business data processing technology. The method includes: receiving a business change message; parsing the business change message and extracting the changed fields where data updates have occurred; determining at least one target business node affected by the changed fields based on the predefined declaration information of multiple business nodes; performing topological sorting on all target business nodes to generate an execution sequence; and sequentially executing each target business node in the execution sequence to output updated data. This invention can identify the target business nodes affected by the changed fields, avoiding calls to irrelevant nodes and changing the original full-process calculation mode to a dynamic orchestration mode triggered by changed fields. This directly reduces unnecessary calculation steps and external interface calls, lowers load and resource consumption, and enhances adaptability to complex business scenarios while ensuring timely data output.

Data set is the basis of deep learning, and building a data set close to the real scene has an important influence on the deep learning model, but there is a data cascade situation, which makes the trained model not well applied to the real scene. The invention wants to solve how to provide a method for making the data set closer to the real scene in the case of data cascade. The data set used for training the model is taken as the training set, and the real scene sample is taken as the test set. The features of the training set and the test set are extracted respectively, the inter-domain similarity between the training set and the test set is calculated, and the intra-domain uncertainty of the training set is calculated, and the comprehensive similarity is obtained by comprehensively calculating the inter-domain similarity and the intra-domain uncertainty. The comprehensive similarity can be used to select good samples in the training set, and the model trained by the good samples has better performance and reduces the influence of data cascade. The method is verified through experiments.

This application provides a data replication method, apparatus, electronic device, and computer storage medium, relating to the field of database technology. The method includes: acquiring at least one binary log from a database to be replicated; traversing transactions in the binary log; for each target transaction traversed, determining whether the target transaction's position information is stored in a preset first database table; if it is determined that the target transaction's position information is not stored in the first database table, then executing the target transaction in the target database and recording the target transaction's position information in the first database table, until all transactions in the binary log have been traversed. This application can prevent the same transaction from being executed multiple times in the target database, preventing data corruption even in the event of program failure, and ensuring reentrancy of operations during data replication, guaranteeing data integrity even in the event of multiple failures.

The embodiment of the application discloses a data synchronization method and related device, wherein the method is applied to a target data center under a multi-active data center architecture, the target data center is any one of a plurality of data centers under the architecture, and the plurality of data centers have respective corresponding databases. The method comprises the following steps: receiving to-be-synchronized operation data sent by other data centers, the to-be-synchronized operation data being configured with version information, the version information being determined according to generation time of the to-be-synchronized operation data; determining whether the to-be-synchronized operation data is valid according to the version information of the to-be-synchronized operation data and reference operation data; if it is determined that the to-be-synchronized operation data is valid, performing target data-based change processing in the corresponding database according to the to-be-synchronized operation data; and if it is determined that the to-be-synchronized operation data is invalid, ignoring the to-be-synchronized operation data. The method can solve the problem of data miscoverage caused by data conflicts and ensure complete synchronization of data between the data centers.

The application discloses a sequence stratigraphic division method based on imaging logging, which comprises the following steps: acquiring imaging logging original data, generating borehole wall image data and extracting stratum occurrence data containing depth and dip angle; comprehensively identifying unconformity surfaces based on the above data and marking the unconformity surfaces as first-type interfaces; calculating and identifying dip angle conversion surfaces, marking the dip angle conversion surfaces as second-type interfaces and third-type interfaces according to dip angle change trends; defining the first-type and second-type interfaces as sequence interfaces and defining the third-type interfaces as maximum flooding surfaces; and drawing a histogram based on the definition result to complete sequence division. The application establishes a quantitative algorithm relationship between a dip angle conversion mode and key sequence interfaces, overcomes the defects of poor anti-interference ability, abstract results and dependence on subjective experience of a traditional conventional logging division method, and realizes high-precision, quantitative and visualized division of sequence strata.