139 results about "Multiple factors" patented technology

The application discloses a tunnel grouting diffusion form prediction system and method, which comprises the following steps: a tunnel simulation device is used to prepare a tunnel model with fissures to simulate a tunnel rock mass; a grouting simulation system is used to grout the tunnel simulation device by adjusting grout diffusion influencing factors to obtain grout diffusion states under the influence of multiple factors; a grouting diffusion form quantification system is used to cut the grouted tunnel simulation device to obtain a plurality of longitudinal position slices, divide the surface of each longitudinal position slice into grids, and obtain a grout diffusion form quantification dataset; and a grouting diffusion form prediction system is used to construct a multi-modal spatiotemporal fusion deep prediction model and input the grout diffusion form quantification dataset into the multi-modal spatiotemporal fusion deep prediction model to predict the diffusion form of future grout. The application realizes spatiotemporal prediction of the grouting diffusion form by establishing a prediction model and using the grout diffusion form quantification dataset as a spatiotemporal sequence dataset.

The application discloses a kind of authorization method and system based on multi-factor binding device and host computer software, it is related to embedded device security and software authorization management technical field, and authorization method includes the following steps: S1: upstream vendor securely solidifies core identity information to the read-only storage area of device by factory programming procedure, S2: upstream vendor generates authorization file for device by binding authorization management procedure according to the authorization data of primary customer, S3: primary customer uses the authorization file obtained from upstream vendor, and performs binding operation on device by device binding procedure;With device to establish communication, S4: end user connects host computer software and device, controls device to run;Authorization system includes factory programming procedure, binding authorization management procedure, device binding procedure, host computer procedure.The beneficial effects of the application include realizing the leap from "two-dimensional binding" to "multi-dimensional binding", and providing more control granularity and flexibility.

This disclosure provides a method and apparatus for predicting human-machine conflict, capable of proactively predicting human-machine conflict from multiple dimensions. By acquiring and analyzing various physiological and behavioral data of operators in historical human-machine interaction scenarios, significant characterization factors corresponding to fatigue states are screened, enabling a more accurate quantitative representation of operator status. Simultaneously, the significant characterization factors of operators are combined with environmental and machine characteristic dimensions (including the complexity of interface interaction) to establish a human-machine conflict prediction model suitable for the target scenario. This method can dynamically reflect changes in multiple factors during system operation, proactively predicting the probability of human-machine conflict occurrence, thereby improving the comprehensiveness, timeliness, and reliability of identification, avoiding lag and bias caused by single data or rules, and ultimately contributing to improving the safety and stability of complex human-machine systems.

The present application relates to the cross technical field of geotechnical engineering and machine learning, especially a concrete-sand interface bearing capacity prediction method and system based on machine learning, the method comprising the following steps: obtaining core input parameters to obtain multi-working condition test variables; preparing a concrete sample, constructing a multi-working condition direct shear test through a test device; carrying out the multi-working condition direct shear test and collecting data during the test to obtain a multi-dimensional original data set; obtaining a modeling data set according to the multi-dimensional original data set, combining machine learning to construct a concrete-sand interface bearing capacity prediction model; performing model optimization and generalization capability verification to obtain a bearing capacity prediction optimization model; obtaining a bearing capacity prediction result according to the bearing capacity prediction optimization model, and obtaining a characteristic influence analysis result. The present application studies the concrete-sand interface shear resistance bearing law under the coupling action of multiple factors, and provides a scientific basis for the design and construction of geotechnical engineering.

This invention provides a method and system for evaluating the economic benefits of distributed resource projects throughout their entire lifecycle, involving multiple factors. The method includes: constructing a multi-dimensional basic parameter library for distributed resource projects; collecting operational simulation data of the projects based on the basic parameter library; establishing a multi-factor coupled full-cycle economic benefit evaluation model based on the basic parameter library and operational simulation data, and obtaining the basic benefit calculation result through the coupled calculation of the evaluation model; incorporating financing and loan parameters to dynamically correct the basic benefit calculation result, obtaining the corrected benefit calculation result under the loan scenario; conducting a technical and economic integrated selection evaluation of equipment from multiple manufacturers, and outputting the selection evaluation result; integrating the basic benefit calculation result, the corrected benefit calculation result, and the selection evaluation result to output a refined full-cycle evaluation result and generate a visual report. This invention enables accurate and comprehensive evaluation of the economic benefits of photovoltaic-storage projects throughout their entire lifecycle, providing a reliable basis for investment decisions and scheme optimization.

The application discloses a kind of power line and adjacent structure clearance distance prediction method based on dynamic mechanical model, it is related to electric power overhead line safety assessment technical field.The prior art lacks the clearance distance prior prediction means that can comprehensively consider the coupling influence of multiple factors.The present application comprises: obtaining the basic data containing measured sag;Reverse calculation power line stress under the measurement day working condition;Solve the power line stress under the working condition to be solved using the state equation;Establish three-dimensional dynamic curve model considering the influence of sag and wind deviation;Discrete the curve into dot matrix and calculate the minimum Euclidean distance between the sensitive point, obtain the clearance distance under the working condition to be solved.The present application can determine the minimum clearance distance in the whole life cycle by traversing the list of typical meteorological working conditions and wind direction angle.The present application can realize accurate prediction of any future working condition only once on-site measurement, effectively eliminate design and construction deviation, and provide reliable prior decision basis for power line safety assessment.

This invention discloses a training method and apparatus for a BERT dispatch model based on multi-factor decision-making. The method includes: acquiring raw work order data and preprocessing it; filtering work orders with logical changes to obtain a training dataset; performing initial model training based on the training dataset to obtain an initial dispatch model; constructing a test dataset and performing multi-factor decision-making error analysis; data augmentation; secondary model training to generate an optimized model; iterative optimization and model deployment. This invention improves the dispatch model's ability to learn multi-factor correlation features and adapt to logical change scenarios by standardizing multi-dimensional decision factors, supplementing outdated standard data for cleaning, refining fusion training, conducting specialized error analysis, and expanding data with controllable quality. Simultaneously, it designs an optimized work order filtering mechanism for logical changes, ensuring the model quickly reaches the preset dispatch accuracy standard.

This invention discloses an optimization method for flowback nozzles in deep coal and rock gas formations, considering the influence of multiple factors, belonging to the field of coal and rock gas extraction technology. Addressing the challenge of flowback fluid velocity control in deep coal and rock gas development, this method establishes models for matrix and fracture seepage and two-phase flow within the wellbore. It comprehensively considers the migration of pulverized coal and proppant, as well as the influence of coal seam cleavage fractures, to optimize wellhead nozzle size, reduce the probability of pulverized coal clogging fractures, and improve fracture conductivity. Compared to traditional methods, this invention has significant advantages in improving fracturing efficiency and high-efficiency coal and rock gas extraction, providing an innovative solution for the scientific research and engineering applications of deep coal and rock gas.

The application discloses a geothermal caprock multi-factor coupling evaluation system, which aims to solve the problems of low standardization degree of existing evaluation manual operation process, lack of unified engineering architecture of multi-factor coupling and insufficient result visualization. The application automatically executes caprock comprehensive evaluation on a unified platform through a data acquisition module, a single-factor evaluation module, a coupling model construction module, a coupling score calculation module, a comprehensive evaluation module and a result output module. The system completes single-factor scoring based on basic data, constructs and calculates the scores of four coupling subsystems of lithology-diagenesis-physical property, structure-mechanics-integrity, thickness-continuity and fluid-pressure-temperature, quantifies the coupling relationship between the subsystems by using a cross influence matrix to obtain the final score, and outputs the comprehensive score and the visualized result after weighting. The application realizes the integration and automation of the evaluation process, supports the engineering landing of complex coupling models, and improves the result visualization and decision support capability.

The application provides a method for determining a scheduling mode of reservoir flood control storage capacity released in stages at the end of flood season, and relates to the technical field of reservoir scheduling. The method comprises the following steps: collecting and arranging basic data, including hydrological and meteorological data, information of flood protection objects downstream, and data of reservoir scheduling parameters; determining a release time of reservoir flood control storage capacity based on multiple factor conditions; determining a release discrimination index of reservoir flood control storage capacity according to water level characteristics of a flood control station; carrying out risk analysis and risk atlas drawing of reservoir flood control storage capacity release; and determining a recommended flood control storage capacity release scheduling mode according to the flood control risk atlas. The application determines the release time of reservoir flood control storage capacity and the corresponding discrimination index by comprehensively considering multiple factor conditions, and draws a downstream flood control risk atlas on the basis of flood regulation calculation, and finally determines the recommended flood control storage capacity release scheduling mode in stages, which is of great significance for flood resource utilization at the end of flood season, improvement of reservoir storage guarantee rate, guarantee of downstream flood control safety and water safety.

The application discloses a ship lock concrete structure durability analysis method and system under the coupling action of multiple factors, which comprises the following steps: acquiring internal structure images of different erosion areas of a ship lock concrete test piece; identifying and quantitatively analyzing the internal structure images of different erosion areas by a deep learning image segmentation algorithm to generate an erosion structure factor set; calculating environmental characteristics corresponding to multiple environmental parameters by a multi-physical field coupling inversion algorithm; constructing a three-dimensional microstructure model of different erosion areas; dynamically simulating the three-dimensional microstructure model according to the environmental characteristics; performing spatio-temporal correlation analysis on simulation result data by a hypergraph neural network to generate a prediction result; and comparing an expert knowledge base and performing construction maintenance based on text information of the prediction result by an Euclidean distance similarity algorithm. The application introduces high-precision three-dimensional model multi-factor coupling simulation, and improves the ship lock concrete structure analysis precision through high-precision coupling prediction of the hypergraph neural network.

The application provides a reservoir group water demand prediction and joint optimization scheduling method and system, and relates to the technical field of data processing. Through dynamically combining real-time change parameters such as inflow, initial storage, power generation output, crop growth stage, industrial water-saving link, fine prediction of reservoir cumulative storage, power generation water demand flow, irrigation predicted water demand and industrial predicted water demand is realized. By comprehensively comparing different types of water demand, a larger value is taken as the comprehensive water demand. In the joint scheduling link, from downstream to upstream scheduling strategy, combined with multiple constraint conditions such as water supply priority, water temperature and comprehensive water demand, the scheduling decision of the reservoir group is more scientific and reasonable. The joint scheduling mechanism considering multiple factors can better balance different water demand, improve water resource utilization efficiency, effectively cope with the complexity and uncertainty in water resource allocation, and provide more reliable protection for the optimized operation and multi-objective scheduling of the reservoir group.

The application discloses a regression test case set selection method based on requirement association, comprising: association relationship basic library construction: requirement association matrix is constructed for a plurality of test requirements, and test item-requirement association matrix is constructed, mapping relationship of test case to test item is constructed, priority order of test case is set, and test defect-test case association matrix is constructed; regression test case selection model construction: test defect is associated with test case, test case is associated with test item, test item is associated with test requirement, and test requirements with different relationship strengths are selected for association; regression test case set selection: based on the regression test case selection model, test requirement, test case priority and defect library are combined to obtain the best regression test case set. The application is based on multiple factors for selection of the regression test case set, so that the effectiveness of the selection is higher.

This invention discloses a finite element simulation method and system for a suspension bridge tower top saddle. The method includes: collecting multiple types of parameters; iteratively solving for tangential and normal forces based on an initial tangential stiffness matrix; calculating the actual contact friction coefficient based on the coupling relationship between temperature and contact normal pressure; calculating a tangential correction coefficient based on the actual contact friction coefficient, the ratio of tangential to normal forces, and the linkage relationship between the saddle geometric parameters; dynamically adjusting the tangential stiffness matrix based on the correction coefficient, the saddle pre-offset, and the synergistic relationship between cable stress; constructing a finite element model and simulating it; and iteratively optimizing until convergence. The system corresponds to the modules described above. This invention optimizes simulation accuracy through multi-factor coupling and linkage, adapting to complex working conditions and providing reliable support for the design and construction of suspension bridges.

This invention provides a method and system for early warning of debris flow initiation based on dynamic weighting of multiple influencing parameters, belonging to the field of geological disaster prevention and early warning technology. The method includes: constructing a set of multiple influencing parameters by acquiring source conditions, topographic features, and real-time rainfall parameters; constructing an intrinsic weighting function based on the initiation physical mechanism to calculate the intrinsic weights of the source and topographic parameters; adjusting the intrinsic weights using a dynamic correction function with rainfall intensity or cumulative rainfall as input to obtain dynamic weights; establishing a critical function coupling the dynamic weights and critical conditions to calculate the debris flow initiation critical index, and issuing an early warning when the threshold is exceeded. This solves the problem that existing debris flow early warning methods are unable to simultaneously integrate physical mechanisms and dynamic weight adjustment, and accurately reflect the critical state of debris flow initiation under the coupling effect of multiple factors.

The application relates to a kind of military target selection methods based on multi-factor dynamic trade-off, comprising the following steps: step S1: quantifying military target importance, realize the military target selection based on multi-factor dynamic trade-off;Step S2: determine the initial importance of target, introduce combat resource constraint coefficient, step S3: introduce battlefield situation influence factor, update the target importance after considering timeliness;Step S4: combined with the target importance after adjustment, calculate the comprehensive priority of military target, execute combat operation;Step S5: after completing target selection, further combined with relevant combat background, determine target capability index, calculate the target capability vector and matrix before and after striking, evaluate the striking effectiveness of military target. Through dynamic mathematical model real-time adjustment target priority, realize more scientific, accurate selection of military target, effectively improve the efficiency of military resource utilization and combat effectiveness.

The application discloses an experimental test and evaluation method for simulating breakage of a nuclear fuel rod and escape characteristics of fission products, which comprises consideration of similarity criteria, and ensures that the cylindrical geometry of the broken fuel rod can simulate the flow characteristics of the flow field near the break along the axial direction of the cylindrical surface; and the flow similarity in the experimental section is realized by Re>10 5 The flow field enters the self-modification area; the critical break diameter is determined based on the blocking flow theory; the specific experimental conditions are determined by single variable control processing of multiple factors; the release rate of fission products per unit time and the escape rate of fission products finally entering the coolant are divided into two experiments of fuel rod constant internal pressure and fuel rod sealing for measurement; and high-confidence uncertainty evaluation is carried out by using A and B two types of evaluation components; and the experimental results are used to fit the fission product escape rate coefficient model. The application solves the problems of poor reliability of experimental extrapolation, blind selection of working conditions and incomplete evaluation system, and provides high-precision benchmark data for nuclear safety analysis programs.

This application relates to a method for calculating the internal pressure and output force of a hydraulic muscle using multi-factor coupling, comprising the following steps: obtaining the initial geometric parameters and rubber material parameters of the hydraulic muscle; constructing a multi-factor coupled output model based on the initial geometric parameters and rubber material parameters; and obtaining the input parameters of the hydraulic muscle, including the internal pressure data of the hydraulic muscle at the previous moment, the hydraulic oil input flow rate data at the current moment, the length data at the current moment, and the equivalent bulk modulus of elasticity data. This method, by constructing a multi-factor coupled output model, achieves high-precision calculation of the internal pressure and output force of the hydraulic muscle, significantly improving the physical realism of the simulation results. Furthermore, the initial geometric parameters and rubber material parameters required for model construction can be obtained by consulting product manuals or through simple measurements, solving the core pain points of existing data-driven methods, such as poor versatility and high calibration costs.

This invention provides a method and apparatus for ultra-short-term wind power prediction based on an intelligent hybrid model, belonging to the field of wind power prediction technology. The method includes: extracting influencing factors from a historical power database of wind farms; designing an intelligent hybrid model architecture based on a set of wind power influencing factors; introducing an attention mechanism to perform multi-scale feature extraction and wind power prediction training on the historical power database based on the wind power influencing factor set and the intelligent hybrid model architecture; dynamically weighting multiple types of ultra-short-term wind power factor prediction models; and generating an ultra-short-term wind power prediction hybrid model for closed-loop wind power prediction. This invention solves the technical problem of low prediction accuracy and large fluctuations in wind power prediction when influenced by multiple factors due to the existence of single models or static prediction methods in existing technologies. It achieves the technical effect of improving the accuracy and stability of ultra-short-term wind power prediction through the fusion and dynamic weighting of multi-source factors in an intelligent hybrid model.

The present application relates to the technical field of humic acid, and particularly relates to a humic acid yield prediction method based on machine learning, comprising: collecting raw material characteristic parameters, raw material ratio and experimental condition parameters as input features; taking humic acid yield as an output feature; constructing a data set; constructing three quantitative models of RF model, SVM model and XGBoost model, training the quantitative models by using the training set data, and optimizing the hyperparameters of the quantitative models by using grid search combined with 5-fold cross-validation. The present application solves the problems of high cost, long cycle and difficulty in comprehensively investigating the interaction of multiple factors in the traditional humic acid preparation process optimization which depends on trial and error experiments.

The application discloses a kind of full-automatic process simulation method for product design, multiple-factor influence, multiple-target optimization, comprising:1, the data interaction between Creo Parametric and Ansys is established;2, parameterized model is established in Creo Parametric;3, parameterized model is imported into Ansys, meshing, case setting and result processing are carried out, and visual result is obtained;4, based on visual result, DOE experimental design is carried out in Desigin-Expert, and initial sample space is generated;5, initial sample space is imported into the parameter set of Ansys, parameterized simulation is carried out, and initial sample space target value is obtained;6, initial sample space target value is imported into Design-Expert, and target function between multiple factors and multiple targets is fitted;7, based on multiple-target optimization algorithm and weight setting, multiple-target optimization is carried out, and multiple-target optimization result is obtained;8, multiple-target optimization result is returned to Ansys and simulation verification is carried out.The application realizes the automation and process simulation under different structure parameters, and saves simulation time.

This invention discloses a method, equipment, and storage medium for selecting favorable areas of low-permeability carbonate gas reservoirs based on intuitionistic fuzzy multi-attribute boundary approximation region comparison (MABAC). Specifically, it includes the following steps: S1, screening key indicators of carbonate gas reservoirs for favorable area selection; S2, determining attribute weights based on the Brown-Warshall Method (BWM); S3, establishing a favorable area selection model for low-permeability carbonate gas reservoirs using the intuitionistic fuzzy multi-attribute boundary approximation region comparison method (MABAC); S4, calculating conditional probabilities, and then ranking the decision objectives included in the positive, boundary, and negative domains to obtain the favorable area selection results. This invention can simultaneously consider the influence of multiple factors on different schemes and grasp the priority relationship between factors.

The present application relates to a kind of navigation integrity evaluation method and system of comprehensive multi-factor influence, belong to satellite navigation integrity monitoring technical field, solve the problem that focusing single rough error processing risk in prior art, lack multi-factor collaborative influence analysis, cannot truly reflect the problem of positioning uncertainty.Method includes: obtaining observation dataset, ephemeris data and receiver approximate three-dimensional coordinates, constructs function model, calculates final correction number approximation, determines final posterior standard deviation;Based on all rough error containing observation value, calculate the maximum rough error clustering number, determine multi-rough error processing risk;Based on observation dataset, calculate single-system positioning result, determine ambiguity fixing error risk;According to final correction number approximation, final posterior standard deviation, multi-rough error processing risk and ambiguity fixing error risk, determine navigation integrity evaluation result.The present application realizes navigation integrity evaluation under the influence of multi-factor, improves the quantitative accuracy of the uncertainty of positioning result.

This invention discloses a machine learning-based method and system for predicting particle migration and blockage in fractures. The method includes the following steps: First, preset data on fracture roughness coefficient (JRC), flow velocity, equivalent particle size, and particle number; construct a fracture model with realistic shape and conduct a visualization migration experiment; record fracture blockage label data; input the parameters and labels into a neural network binary classification probability model with a multilayer perceptron as its core, and obtain a blockage probability prediction function through training; based on the trained model, perform sensitivity analysis and feature importance evaluation, and output the ranking of the impact of blockage probability on JRC, flow velocity, equivalent particle size, and particle number, thereby achieving the prediction of particle blockage events within fractures. This invention studies the particle blockage mechanism inside fractures under the coupling effect of multiple factors, providing a scientific basis for the design and construction of geotechnical engineering.

The application relates to a recycled aggregate concrete creep coefficient prediction method and device, the method constructs a creep coefficient prediction model according to a CNN-BiLSTM, then determines hyperparameters, optimizes the hyperparameters according to an enhanced sparrow search algorithm, inputs creep influencing factor data into the creep coefficient prediction model according to the optimized hyperparameters, obtains a creep coefficient prediction value, and trains the creep coefficient prediction model based on the creep coefficient prediction value and a measured value; in the application stage, target creep influencing factor data is input into the creep coefficient prediction model to obtain a target creep coefficient. Through combination of CNN-BiLSTM learning of a complex nonlinear mapping between influencing factors and creep, and optimization of model hyperparameters by using the enhanced sparrow search algorithm, the problems of strong coupling and high nonlinearity of multiple factors in RAC creep coefficient prediction are solved, and fast and accurate prediction of the RAC creep coefficient is realized.

This invention discloses a control method and system for incomplete multi-view data, belonging to the field of data recovery control technology. The invention extracts monitoring data features from each view and evaluates their quality; views below a threshold are marked as incomplete. Initial recovery results are then obtained by migrating samples from similar neighborhoods of other views. All features are mapped to a unified latent space to obtain fused semantic representation features. By comprehensively considering multiple factors such as missing data labels, data quality, spatiotemporal features, and semantic consistency, the fusion impact of each view is quantified. Then, based on the relationship between the quantified value and a set threshold, the fusion weights of each view are adaptively calculated. Finally, a global fusion representation feature is generated based on this weighted average, thereby reconstructing and recovering the incomplete view. Anomaly detection is performed, and alarm information is output, improving the accuracy of incomplete view data recovery results and solving the problem of reduced accuracy in existing technologies.

This invention discloses a method and system for comparing bridge maintenance schemes based on deep learning. The method includes: determining alternative maintenance schemes for the bridge to be maintained; collecting historical data of the bridge maintenance project and processing it to obtain training and testing sets for each maintenance feature; training a corresponding deep learning model for each maintenance feature to learn the relative importance of each maintenance feature under the combined influence of multiple factors; calculating the objective weights of each maintenance indicator using the trained model and the testing set; calculating the value index of each alternative maintenance scheme based on the weights, and determining the scheme with the highest value index as the optimal maintenance scheme; and generating a quantitative decision report based on the optimal maintenance scheme to support scheme approval and construction. This invention achieves objective quantification of maintenance indicator weights through a deep learning model, replacing traditional subjective or simple statistical weighting methods, and improving the objectivity, economy, and intelligence of bridge maintenance decision-making.