16 results about "Correlation method" patented technology

The invention discloses an optical observation correlation method based on mixed multi-hour ephemeris, and belongs to the technical field of space target monitoring. Aiming at the problem that the correlation performance is reduced when the ephemeris quality is unstable due to dependence on a single ephemeris in the prior art, the method comprises the following steps of: firstly, constructing a mixed set containing multi-source ephemeris; secondly, intelligently selecting a leading ephemeris and a lagging ephemeris which are closest in time for each observation arc section and each target; then, the two ephemeris are used for association respectively, and union sets of results are obtained, so that an initial association set is expanded; and for the generated fuzzy correlation, performing primary disambiguation based on the comprehensive confidence, and performing secondary accurate disambiguation on the residual fuzzy condition by comparing the weighted angular distance residual error root-mean-square value of the whole arc section. According to the method, the dependence on single-term and single-source ephemeris precision is effectively reduced, available information is fully utilized, and the correlation robustness and accuracy of optical observation data under the complex ephemeris condition are remarkably improved.

The invention provides a product full life cycle data model construction and association method, system and device and a medium, and relates to the field of weapon equipment digitization. According to the method, a data model architecture is determined by analyzing light weapon full life cycle data, and a unified modeling language is used for carrying out model building on a meta-model layer, an abstract model layer and an object instance layer. On the basis of a constructed light weapon development and production process data model, a light weapon product data association rule is constructed from the perspective of application. Based on the constructed association rule and the light weapon product data model, combining the data with the association rule, constructing a light weapon product data association relationship, and associating the light weapon product data through unique identifiers such as a product code, a batch number, a system code and the like. The method provides a basis for application of quality problem tracing, rapid fault diagnosis and positioning, product digital resumes, knowledge maps and the like in the whole life cycle, and the light weapon whole life cycle data acquisition efficiency can be effectively improved.

The application discloses a track correlation method based on improved topology, which comprises the following steps: S1, converting two radar target tracks into a common coordinate system; S2, constructing a topology sequence of the radar target tracks; S3, combining historical topology information to calculate the topology average distance of the target at the current moment; S4, using the OSPA distance to measure the correlation degree of the target topology set to obtain a correlation matrix; and S5, using the Hungarian algorithm to perform optimal distribution on the correlation matrix to obtain a correlation result. The track correlation method based on the improved topology can effectively reduce the influence of random errors on the track correlation algorithm and improve the correlation performance.

The application provides a satellite in-orbit anomaly and space environment correlation method based on density distribution drift, comprising the following steps: S1: determining a typical repetitive anomaly of an in-orbit spacecraft, on the basis of which, it can be considered that the event has consistent environmental correlation characteristics, so that it can be statistically analyzed together and further analyzed; S2: using the occurrence of the abnormal event as a condition, the space environment data is divided into two parts, and the probability density distribution of the two parts is counted; S3: according to the difference between the probability density distribution of the two parts in S2, the influence of the space environment is formed to form an explainable correlation characteristic, and the correlation analysis is completed. The method is suitable for analyzing the in-orbit repetitive anomaly phenomenon, and the possible correlation characteristics between the change characteristics of the space environment and a kind of repetitive in-orbit anomaly phenomenon are analyzed by using the distribution characteristic construction method in statistics.

The application discloses an industrial product quality risk correlation method based on a graph neural network, relates to the field of industrial manufacturing, and comprises the following steps: collecting multi-source industrial data, preprocessing the multi-source industrial data, obtaining a standardized data table, constructing an industrial element heterogeneous graph based on the standardized data table, inputting the industrial element heterogeneous graph into a graph attention network base model for node representation learning, setting a product batch node in the industrial element heterogeneous graph as a prediction target, and setting a historical quality detection result as a supervised label of a supervised learning task, and introducing a meta-path self-learning layer in a message passing process of the graph attention network base model. The application upgrades deterministic prediction to probabilistic inference, provides an inherent confidence measure for each risk prediction value, and realizes automatic, accurate and interpretable correlation analysis and traceability positioning of industrial quality risks.

The present application relates to the technical field of smart grid, disclose a kind of multi-factor associated return home degree winter power load pre-control method and system, method includes: the historical power consumption data obtained in advance is transformed to obtain power load data, and power load data is identified to obtain power consumption problem;The influencing factor is obtained by analyzing power consumption problem, and the strong correlation factor data matrix is obtained by analyzing influencing factor, and the correlation influence coefficient is obtained by analyzing strong correlation factor data matrix;The trained power load prediction model is obtained by iterative training to power load prediction model, and the current power consumption data obtained in advance is input into trained power load prediction model to obtain load prediction data;The red yellow green classification area is obtained by dividing power grid;Power load pre-control scheme is formulated, so as to carry out power consumption prevention and control management.The present application can solve the problem that power grid overload is difficult to prevent and control during return home degree winter and power consumption prediction is not accurate enough.

The application provides a relaxation and creep performance correlation method, and belongs to the field of new materials and is used for developing new materials by testing related activities. First, a high-temperature relaxation test is carried out to obtain corresponding data of temperature-stress-creep relaxation rate. Second, a high-temperature creep test is carried out to obtain minimum creep rate data. Finally, the third step is data processing, and a correlation model is constructed. The application aims at the shortcomings of long data obtaining time and large sample consumption of existing creep performance data, and proposes to use the characteristics of short stress relaxation test, i.e., more creep information and less sample consumption, to quickly obtain a large amount of stress relaxation performance data, and then combine the correlation method shown in the application to quickly and massively obtain the creep performance, so that the service performance can be evaluated.

The application discloses an AI auxiliary-based natural resource factor cross-layer index correlation method and system, which comprises the following steps: firstly, a set containing multiple remote sensing images is acquired, and the set and to-be-matched natural resource factor information are loaded into a pre-trained multi-modal ground feature correlation model to respectively obtain remote sensing image features and description features; and a target remote sensing image is determined by calculating a matching degree. Different layer data is obtained by separating a target image layer, layer features are extracted, and correlation with factor index features is established, thereby constructing a cross-layer index correlation model, and effective correlation of natural resource factor cross-layer indexes is realized.

The present specification provides a high-precision stratigraphic correlation method, device and electronic equipment, which constructs a filtering factor through well logging data, and then extracts a high-frequency seismic data body and a low-frequency seismic data body from a seismic data body by using the filtering factor. Through time-varying seismic wavelet deconvolution on the high-frequency, medium-frequency and low-frequency seismic data bodies, a target data body with a "virtual resolution" and a much wider frequency band than the seismic data body can be obtained, so that the profile data of the target data body can be used to more accurately distinguish the interwell lateral characteristics, and the stratigraphic correlation is adjusted based on the interwell lateral characteristics to realize high-precision stratigraphic correlation. The high resolution and lateral variation characteristics of the target data profile are used to finely carry out stratigraphic correlation, thereby providing protection for fine exploration and development of oil and gas fields, especially development geology research.

The application relates to the technical field of knowledge base fusion, and particularly relates to a knowledge base data fusion correlation method and system based on an AI large model. First, original data is collected through a connector, then washing and deduplication processing are carried out, then field names are vectorized and aligned, an AI large model is used to extract entities and attributes, entity conflicts are solved through a density clustering algorithm and an entity similarity threshold, relationship triplets are generated, uniform identification is assigned to entities and attributes for standardized processing, data conflicts are detected through a coefficient of variation, and parameters are dynamically adjusted to optimize a fusion strategy, finally, a vector index is constructed through a graph database, and a graph download interface is provided. On the basis of realizing data fusion, the application skillfully solves the problem that in the existing database fusion method, when facing data conflicts, it is difficult to specifically identify the reasons and give solutions, significantly improves the accuracy and reliability of knowledge base data fusion, and has important innovative significance and practical value.

The present application relates to the technical field of data analysis, and particularly relates to a multi-modal data adaptation and feature correlation method for intelligent insight, comprising the following steps: standardizing and arranging structured data fields in data sources of different systems to form a structured field set, identifying field drift patterns, and generating a structure drift label set; guiding semantic flux analysis on content related to the structured field in unstructured modal data, and extracting core semantic candidate units; finally screening out a standardized target semantic unit set; performing graph structure alignment on the standardized target semantic unit set and the structured field set to obtain a field evolution graph, and performing dynamic adaptation based on field inheritance relationships and time sequence evolution paths in the field evolution graph. The present application effectively excludes pseudo-related expressions that are only close in static semantics but have inconsistent contexts, and improves the authenticity and business effectiveness of semantic unit screening.

The invention discloses a sea-gas interface momentum flux calculation method coupled with boundary layer meteorological conditions, and relates to the technical field of marine atmospheric boundary layer flux observation, comprising: performing threshold screening and outlier removal processing on turbulence flux elements required by vortex correlation method calculation, and performing coordinate rotation on three-dimensional wind speed; processing the obtained sea surface fluctuation data to obtain a sea wave spectrum and wave parameters; on the basis of the Monin-Obhoff similarity theory, the Monin-Obhoff length, the atmospheric stability and the wind speed U10 of 10 meters on the sea are calculated; based on the data, splitting the friction speed into a wind speed item and a wave mixing item for fitting, and finally obtaining momentum flux; on the basis of guaranteeing the flux calculation precision, the requirement for an observation instrument is lowered, accurate calculation of the momentum flux of the air-sea boundary layer is achieved, and the requirements for momentum flux data in the fields of marine meteorological research, weather forecast, marine resource development and the like are met.

The invention belongs to the technical field of sea surface target association, and particularly discloses a sea surface target association method based on feature topological graph matching. The method comprises the steps of generating a feature topological graph of a to-be-associated target based on target sea surface distribution detected by a detector; two to-be-associated targets detected by different detectors form a to-be-associated target pair, and through rotation and translation, the number of reference target pairs meeting neighborhood overlapping in two feature topological graphs of the to-be-associated target pair is the maximum; the matching degree of the reference target pairs is evaluated according to the distance between two reference targets in the reference target pairs, and the matching degree of the target pairs to be associated is evaluated according to the matching degrees of all the reference target pairs; and finally, iteratively evaluating the matching degree of all the target pairs to be associated, and associating the target pair to be associated with the optimal matching degree as the same target. The sea surface target association method does not depend on absolute position information of the target, and is higher in adaptability and robustness.

The invention discloses an asynchronous track association method, device, medium and system, and belongs to the field of distributed multi-sensor multi-target tracking track management, and the method comprises the steps: S1, carrying out the association statistical test of an association period on two pieces of received asynchronous track data: if the association period ends and the track separation quality exceeds a threshold value, directly judging that the two tracks are not related, and if the track separation quality exceeds the threshold value, directly judging that the two tracks are not related; otherwise, carrying out re-checking period correlation statistical test; and S2, performing re-check period correlation statistical test on the two tracks passing through the correlation period: if the re-check quality of the tracks exceeds a threshold value after the re-check period is ended, the two tracks are considered to be correlated, and otherwise, the two tracks are judged to be uncorrelated. The technical problem of track error accumulation caused by time registration is avoided.

The invention discloses a low-orbit target association evaluation method based on optical observation, and belongs to the technical field of space target monitoring. In order to solve the problems of high false alarm rate and poor robustness caused by neglecting a distance factor in a traditional fixed angular distance correlation method, the method comprises the following steps: firstly, setting a nominal detection distance and a corresponding nominal angular distance residual error threshold; secondly, aiming at potential associated targets of different distances, dynamically calculating an exclusive associated threshold value according to a formula that an angular distance residual error threshold value = a nominal threshold value * (nominal distance / target distance), and reflecting a principle that an angular distance residual error caused by an ephemeris forecast error is inversely proportional to the distance; and finally, in order to prevent the correlation threshold of the extremely close distance target from being too wide, setting a maximum angular distance threshold for constraint. According to the method, the association confidence of different distance targets is unified through the distance self-adaptive dynamic threshold, the association false alarm rate is remarkably reduced, the tolerance to ephemeris forecast errors is improved, and the accuracy and robustness of optical observation data association are effectively improved.