11 results about "Statistical relation" patented technology

The invention discloses a composite foundation design method based on the influence of soil around a pile on the strength of a gravel pile and application, and the design method comprises the steps that each soil layer is subdivided, and the relation between the undrained shear strength value and the depth of each subdivided soil layer is recorded; performing statistics on the data by adopting a mean square error method according to the dynamic sounding hammering number data of the gravel pile, so as to obtain the dynamic sounding hammering number N of each subdivided soil layer; counting the statistical relationship between the undrained shear strength value of each soil layer and the dynamic sounding hammering number N of the gravel pile, and evaluating the compactness of the soil layer; crushing gravel with different compactness through a large direct shear test or a large triaxial test, and establishing a relationship between the different compactness of the gravel and an internal friction angle; establishing an empirical relationship between the strength of the soil around the pile and the value of the internal friction angle of the gravel; and composite foundation parameters are selected according to the subdivided soil layer parameters. The internal friction angle parameter of the gravel pile can be obtained in a layered mode according to the distribution of the soil layer, and the actual mechanical property can be better reflected.

An apparatus and method for predicting fungible asset requirement using statistical relationship modeling. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to process a plurality of multimodal data associated with a first fungible asset. The memory instructs the processor to generate, using a correlation module, a correlation matrix as a function of the plurality of multimodal data. The memory instructs the processor to generate a prediction module as a function of the correlation matrix. The memory instructs the processor to generate at least an acquisition outline for a second fungible asset using the prediction module. The memory instructs the processor to transmit the at least an acquisition outline to a downstream device.

The invention relates to the technical field of archive storage, in particular to an intelligent archive classified storage system which comprises a character arrangement analysis module, a rule priority judgment module, a classified number registration module, a storage position extension module and an archiving path generation module. According to the method, the statistical relationship between the number of times of rule triggering and the number of times of misjudgment is constructed, the priority ranking is carried out according to the rule suitability, the dynamic nature and classification accuracy of rule scheduling are enhanced, a number hierarchy weight calculation and structure offset comparison strategy is introduced in the archiving number judgment process, the number registration precision and the structure matching degree are improved, and the efficiency is improved. According to the method, when the capacity of the archiving block is close to overflow, dynamic combination and boundary updating of the adjacent empty areas are achieved, the ductility of a classified storage structure is kept, the serial number difference value and the track moving path are subjected to combined sorting comparison in archiving path generation, the archiving calling path and the archiving execution time are shortened, and the archiving efficiency is improved. And the filing efficiency and the number matching stability are improved.

The embodiment of the invention provides an intelligent prospecting method and device based on a data control theory, and the method comprises the steps: building a variable relation graph through collecting multi-modal original mineral exploration data, taking each exploration variable as a node, taking a statistical relation between the variables as an edge, and carrying out the calculation of the multi-modal original mineral exploration data through the guidance of a dissipation structure theory; carrying out centrality measurement on the variable relation graph through a graph neural network to obtain a key variable subset in the original mineral exploration data; performing feature dimension reduction on the key variable subset to construct a feature parameter space based on the principle of Hapkine, predicting a feature state in the feature parameter space through an ordinary differential equation, and outputting a corresponding feature parameter after the state is verified through a particle filter algorithm; based on guidance of the sharp point mutation theory, the characteristic parameters serve as control parameters to be input into a preset physical information neural network, state judgment is conducted through a state discriminant, a prospecting decision signal is determined, and the accuracy and efficiency of mineralization decision can be improved.

This invention relates to the field of archival storage technology, specifically an intelligent archival classification and storage system. The system includes a character layout parsing module, a rule priority determination module, a classification number registration module, a storage location extension module, and an archiving path generation module. In this invention, by constructing a statistical relationship between the number of rule triggers and the number of misjudgments, and prioritizing rules according to their adaptability, the dynamics of rule scheduling and classification accuracy are enhanced. In the archiving number determination process, a strategy of calculating number hierarchy weights and comparing structural offsets is introduced, improving the accuracy of number registration and structural matching. When the archiving block capacity is close to overflow, dynamic merging and boundary updates of adjacent empty areas are implemented, maintaining the extensibility of the classification storage structure. In the archiving path generation, the number difference and the track movement path are jointly sorted and compared, shortening the archival retrieval path and archiving execution time, and improving archiving efficiency and number matching stability.

This application provides an intelligent mineral exploration method and apparatus based on data control theory. The method includes: collecting multimodal raw mineral exploration data, constructing a variable relationship graph with each exploration variable as a node and the statistical relationship between variables as an edge, and using dissipative structure theory as guidance to measure the centrality of the variable relationship graph through a graph neural network to obtain a subset of key variables in the raw mineral exploration data; based on the Haken enslavement principle, performing feature dimensionality reduction on the subset of key variables to construct a feature parameter space, and predicting the feature states in the feature parameter space through ordinary differential equations. After the state is verified by a particle filtering algorithm, the corresponding feature parameters are output; based on the cusp catastrophe theory, the feature parameters are input as control parameters into a preset physical information neural network, and the state is judged through a state discriminant to determine the mineral exploration decision signal. This application can improve the accuracy and efficiency of mineralization decision-making.

The application discloses a method, system, device and medium for predicting the development degree of volcanic rock cracks, and relates to the technical field of volcanic rock oil and gas reservoir exploration. The method comprises the following steps: determining a three-dimensional fusion seismic attribute body of a target area; determining a three-dimensional volcanic rock lithology data body according to the three-dimensional fusion seismic attribute body and a lithology division criterion; determining a crack development rate data body predicted by a thickness factor and a crack development rate data body predicted by a control area fault according to the three-dimensional volcanic rock lithology data body, a statistical relationship curve determined based on a sampling point and a drilling identification result and a well point fusion seismic attribute; and performing weighted calculation on the two crack development rate data bodies to obtain a final crack development rate data body. The application can effectively predict the crack development rate of volcanic rock cracks and provide support for the quantitative evaluation of the crack development degree.

The invention provides a seabed slope failure probability evaluation and analysis method based on a BP neural network algorithm, and the method comprises the steps: simulating the stability of a seabed slope in two scenes of rock-soil parameter uncertainty and wave load uncertainty through a random finite element numerical value, and obtaining the safety coefficient or stability label of the seabed slope. Geotechnical parameters and wave load uncertainty parameters are used as input, a safety coefficient or a stability label is used as output, a training set of a machine learning agent model is jointly formed, the two scenes are analyzed and trained through a machine learning BP neural network algorithm, and the two scenes are analyzed and trained. Complicated and time-consuming Monte Carlo random finite element numerical simulation is converted into a statistical relationship, a proxy model for rapidly calculating the seabed slope failure probability is established, the calculation cost of seabed slope reliability evaluation is remarkably reduced, a basis is provided for seabed slope engineering decision making, and the safety of seabed slope engineering is better guaranteed.

A system and a method for privacy preserved and fair synthetic data with proofs is disclosed The system a processor, and memory with instructions to: receive seed data with differing schemas; combine samples into a schema-consistent dataset by reconciling names, types and missing semantics; encode columns by characteristics to optimize representation; predict training requirements (batch size, learning rate, convergence) to optimize training; convert encoded data to training format; train generative models to produce synthetic records preserving statistical relations; sample models to produce records that maintain mathematical consistency with key statistics; generate proofs validating fairness and accuracy between synthetic and real records; assess privacy and fairness risks via attribute sensitivity and representation ratios and use results to guide parameters and sampling; and present via a user interface about job statuses, fairness indices, privacy scores, utility metrics and volumes to a user for governance.

The invention discloses a single dual-polarization radar data self-filling method based on physical constraint and dynamic optimization, and relates to the technical field of data filling. Comprising the following steps: acquiring original observation data, preprocessing the original observation data, and calculating a beam blocking rate; establishing a reliability criterion, and selecting an optimal filling path for each pixel point; when the relative differential phase is determined to be reliable, constructing a core path based on a-relationship; when the differential phase is not reliable but the differential reflectivity is reliable, an auxiliary path is constructed based on a statistical relationship, and data filling is executed hierarchically; and solving a reflectivity field which is finally filled and is physically coherent by minimizing a cost function. Smooth transition of the filling area and the surrounding effective observation area is ensured, and a high-quality data field is formed.

The invention discloses a method for reducing climate mode estimation uncertainty based on variable coordination relationship constraint, which comprises the following steps of: identifying a physical coordination relationship among key variables, and constructing a regression model of multi-mode historical constraint variable data and future constrained variable data; and forming a statistical relationship, namely an emergence constraint equation, which can be checked by the reference data, and further accurately calculating the uncertainty of the corrected global climate mode estimation data according to the multi-model data layer, the reference data layer and the constraint relationship layer in combination with the reference data. According to the method, a physical constraint mechanism is established by mining the cooperative relationship among climate variables, and the uncertainty is calculated by adopting layering, so that the reliability of mode estimation is remarkably improved, and the uncertainty is reduced.