23 results about "Influence coefficient" patented technology

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 discloses a numerical control machine tool precision distribution method from geometric error to manufacturing tolerance, comprising: using small deformation hypothesis and superposition principle to obtain the influence coefficient matrix of manufacturing tolerance to position related geometric error, and combining with the machine tool motion chain to establish a manufacturing tolerance-geometric error-space error three-layer mapping model; adopting Sobol total effect sensitivity analysis of the overall workspace characteristic trajectory to obtain the total effect sensitivity index of each manufacturing tolerance; establishing a manufacturing cost evaluation model, and combining with the total effect sensitivity index to construct a cost sensitivity index; taking the minimum manufacturing cost as the target, comprehensively considering the tolerance definition domain constraint, the sorting constraint and the space error confidence constraint, and using an optimization algorithm to realize precision distribution. The application traces the precision distribution from the abstract geometric error level to the part manufacturing tolerance level, and can further realize the collaborative optimization of machine tool precision design and manufacturing cost.

This application discloses a focusing noise optimization method, apparatus, storage medium, and product, relating to the field of interferometric noise optimization technology. The focusing noise optimization method includes: acquiring an initial interferometric image containing multiple types of focusing noise and inputting it into a preset noise error correlation model to obtain the noise influence coefficients of each type of focusing noise. The noise error correlation model is obtained by training a model to be trained based on interferometric image samples, which include multiple types of focusing noise and actual focusing errors. Based on the noise influence coefficients, a corresponding denoising algorithm is used to remove the focusing noise of the appropriate type. This application enables the model to be trained to learn the relationship between various types of noise and focusing errors, thereby determining the influence coefficients of each type of focusing noise on the focusing errors. Based on the noise influence coefficients, the corresponding types of focusing noise are selectively removed to avoid affecting the wavefront curvature extraction accuracy, thereby improving the focusing accuracy of the wavefront curvature interferometry method.

This disclosure provides a method, apparatus, device, storage medium, and program product for selecting a last-mile delivery station, which can be applied in the field of computer technology. The method includes: acquiring first travel characteristic data of the pickup party from a first starting point to multiple candidate stations; acquiring second travel characteristic data of the sender from a second starting point to multiple candidate stations; determining the influence coefficients corresponding to multiple sets of travel distance data and multiple sets of delivery distance data; and determining a target last-mile delivery station from multiple candidate stations based on the influence coefficients, the multiple sets of travel distance data, and the multiple sets of delivery distance data, wherein the influence coefficients are determined by a pre-trained prediction model.

This invention discloses a method for form finding and force finding of spatial suspension cables using a piecewise linear recursive iterative method, relating to the field of suspension structure technology. The piecewise linear recursive iterative method of this invention divides the cable into n segments. Based on force balance and geometric relationships, the recursive relationship between the internal forces and coordinates from the left end to the right end is obtained. The influence coefficients of the components of the tension at the left end in the x, y, and z directions on the coordinate changes at the right end are obtained. After 3-6 iterations, it converges to a stable value, reaching an equilibrium state. This invention achieves form finding and force finding of spatial suspension cables given the original length and the positions of both ends, with triaxial loads applied to the cable, considering the effects of elastic elongation and temperature. It does not require complex nonlinear calculation software; it can be quickly implemented using only Excel. The calculation operation is simple, with small errors, and is applicable and convenient.

This invention relates to the field of highway maintenance technology, specifically to a highway asphalt pavement performance prediction and maintenance decision-making system and method. It includes the following steps: S1, acquiring pavement images and identifying disease types using a deep learning segmentation network; S2, describing the time-varying evolution of each disease index using a Lotka-Volterra type differential equation system; S3, estimating the parameter set using historical detection data and maintenance records for corresponding time periods through nonlinear least squares inference; S4, given the current disease quantification vector, numerically integrating the differential equation system to predict the change trajectory of each disease index in future time periods; S5, identifying promoting diseases based on the influence coefficient matrix, simulating the dynamic impact of different maintenance measures on the entire disease using the differential equation system, and selecting the optimal maintenance timing and measure combination based on maximizing the comprehensive maintenance benefit function.

The invention provides a building cost estimation method, system and device and a medium, and relates to the technical field of building engineering, and the method comprises the steps: calculating a similarity index of a first project feature of a to-be-built building and a second project feature of a historical project, and selecting a target historical project with the similarity index greater than a preset index; according to the first building cost of the target historical project, generating a second building cost of the to-be-built building; dividing the building to be built into a plurality of construction links, formulating the standard construction progress of each construction link, obtaining the actual construction progress of each construction link, and calculating the progress deviation; obtaining correlation indexes among the construction links, and generating a progress cumulative influence coefficient according to the correlation indexes; and adjusting the second building cost according to the cumulative influence coefficient, and generating the target building cost of the to-be-built building. The method has the technical effect that the deviation between the cost estimation result and the actual cost is reduced.

This invention relates to the field of coating process monitoring and analysis technology, specifically to a coating process anomaly prediction system and method. The method includes: acquiring the core processes of coating production; establishing a data flow model between the core processes based on a multiple linear regression model; optimizing the regression coefficients of the model using Gaussian process regression combined with particle swarm optimization to determine the optimal regression coefficients; performing anomaly propagation path inference based on a Bayesian network path probability inference model; determining the edge weights of directed edges between nodes based on the calculation results of the improved Bayesian formula, and determining the influence coefficients between processes; processing the influence coefficients between processes, determining the predicted abnormal process for each process when an anomaly occurs, and sending notification information. This invention achieves accurate anomaly prediction and efficient notification, enhancing the accuracy and targeting of anomaly prediction, and is more suitable for the anomaly prevention and control needs of complex coating production processes.

The invention relates to a school management analysis method and device for business administration, equipment and a medium. The method comprises the following steps: acquiring business administration data of a school and extracting business administration indexes; based on the business administration indexes, strong association index pairs are determined through correlation analysis, and an association fusion matrix is constructed; grouping the association fusion matrix with similar association influence logic by adopting a clustering algorithm to obtain similar influence groups; based on the similar influence groups, determining similar influence coefficients through a causal relationship between fitting groups; a resource allocation scheme is generated through resource planning allocation by taking the similar influence coefficient as an index priority; and performing scene simulation contrastive analysis by adopting a resource configuration scheme to obtain a school management analysis result. By adopting the method, the school resource configuration efficiency can be optimized, the scientificity and the accuracy of management decisions can be improved, and intelligent analysis and application of the business administration data can be realized.

The invention relates to a fault diagnosis method for an offshore wind power multi-terminal flexible direct current power transmission system based on CNN-Transform. The method comprises the following steps: constructing an initial operation sample data set conforming to the physical characteristics of the offshore wind power multi-terminal flexible direct current power transmission system; constructing an influence coefficient matrix based on the faulty line, generating a fault waveform of the faulty line and a normal waveform of the healthy line based on a transient differential equation, and forming a final operation sample data set; the method comprises the following steps: carrying out preprocessing on a CNN-Transformer model, and inputting the CNN-Transformer model into the constructed CNN-Transformer model to carry out training so as to obtain a trained CNN-Transformer model; and obtaining a to-be-diagnosed operation sample, inputting the to-be-diagnosed operation sample into the trained CNN-Transform model for fault feature and classification, and outputting a fault diagnosis result. Compared with the prior art, the method has the advantages of high accuracy, quick response of fault diagnosis and the like.

This invention relates to the field of supply chain risk management and intelligent decision-making technology, and discloses a method for intelligent prediction and adaptive calibration of supply chain risks by integrating knowledge graphs. The method includes steps such as supply chain knowledge graph construction, multi-source risk data fusion, intelligent risk prediction, adaptive calibration, and risk response optimization. This invention collects real-time operational data of the supply chain through an adaptive calibration step, calculates prediction deviations, and constructs a calibration index system. It dynamically adjusts the weights of prediction model parameters and the influence coefficients of knowledge graph relationships through an adaptive algorithm. It updates the graph rules and model training samples in conjunction with newly occurring risk events. When the calibration accuracy falls below a threshold, it automatically initiates deep calibration and sample expansion, achieving collaborative self-optimization between the model and the knowledge graph, ensuring that prediction accuracy remains at a high level as the supply chain changes. It supports user-defined calibration parameters and thresholds to adapt to the personalized needs of supply chains of different industries and sizes.

The invention relates to the technical field of ardealite resource utilization and process optimization, and provides an ardealite element-mineral correlation and elution difference analysis method which comprises the following steps: acquiring element occurrence data of an ardealite sample before and after elution, and constructing a mineral-element incidence matrix before elution and a mineral-element incidence matrix after elution; based on the pre-leaching mineral-element incidence matrix and the post-leaching mineral-element incidence matrix, respectively constructing a pre-leaching incidence diagram and a post-leaching incidence diagram; inputting the pre-elution association graph and the post-elution association graph into a cross-domain graph neural network model, calculating an element-mineral basic correlation matrix based on node embedding features, and calculating an influence coefficient matrix based on cross-domain attention differences; the basic correlation matrix represents the occurrence relation strength between elements and minerals, and the influence coefficient matrix represents the influence degree of the leaching process on the element-mineral correlation strength. Nonlinear correlation can be captured, the influence of the leaching process can be quantified, and process optimization can be effectively guided.

The present application relates to the technical field of load prediction, and provides a comprehensive energy system load prediction method and system based on multi-task transfer, which comprises: acquiring a load data set, updating a basic load prediction model parameter, and calculating an influence coefficient of each model parameter, and transferring the basic load prediction model parameter to a new load prediction model; acquiring load data of a current time in a new comprehensive energy system operation process, calculating a current time cumulative error, and if the cumulative error is greater than a first threshold value, then updating the new load prediction model parameter according to the influence coefficient, and then using the new load prediction model to perform load prediction; putting the load data of the current time into a new comprehensive energy system data set, calculating a distribution difference between the new comprehensive energy system data set and the load data set, and if the distribution difference is greater than a second threshold value, then adding the data in the new comprehensive energy system data set to the load data set, and re-updating the basic load prediction model parameter. The new load prediction model is ensured to learn the knowledge of the basic load prediction model.

The invention relates to the technical field of steel support axial force dynamic servo regulation and control, and discloses a steel support axial force dynamic servo regulation and control method and system based on deep learning, and the method comprises the steps: collecting node data, filtering data noise, and obtaining purification deformation features. Analyzing a deformation propagation path according to the purified deformation characteristics, calculating a node influence coefficient, constructing an initial deformation matrix, outputting an optimized deformation matrix through a graph neural network in combination with soil characteristic influence parameters of each node, screening high-risk nodes, and fusing environment variables and mechanical difference data of the nodes to obtain an optimal deformation matrix; and generating an adjustment deformation matrix, predicting a node regulation score through linear regression, and generating a priority list. And finally, node stress is analyzed according to the list, an adaptive adjustment instruction set is generated to obtain a preliminary regulation and control draft, and a regulation and control scheme meeting requirements is finally formed through simulation verification and unbalance probability optimization. According to the method, the defect that influence factors in multiple aspects cannot be comprehensively processed and an accurate regulation and control strategy cannot be formulated in the prior art is overcome.

This invention discloses a method for integrated security analysis and control of the main grid and distribution network for online operation sections, comprising: identifying operational security constraints in the power grid based on the acquired current online operation section data, obtaining a set of online over-limit constraints, and forming a set of collaborative control variables; forming a sensitivity matrix by calculating the influence coefficients of each over-limit constraint; constructing a mixed integer linear programming problem with the objective of simultaneously eliminating over-limits of the security constraint set and minimizing the overall adjustment cost of the collaborative control variable set, and generating an integrated joint control scheme; mapping the joint control scheme to the original main grid and boundary equivalent model for accurate power flow calculation and security verification, and obtaining accurate verification results; comparing the prediction results with the accurate verification results, and if the preset convergence criterion is not met, updating the local sensitivity and solving the correction optimization problem based on the current result as the new base point for iterative correction until the final control scheme that meets the security requirements is output.

The invention relates to the technical field of green certificate transaction, and provides a green certificate circulation relation analysis method, device and system and a medium. According to the implementation scheme, variables in a causal loop diagram are extracted, and all the variables and the types of the variables are obtained; executing a Granger causal test on each variable to obtain a Granger causal relationship among the variables so as to construct a causal relationship matrix; based on the type of each variable and the causal relationship matrix, constructing a stock flow graph; performing quantitative estimation on the influence coefficient of each causal link in the stock flow diagram to obtain a target coefficient matrix; based on the stock flow diagram and the target coefficient matrix, constructing a system dynamics model; and performing simulation calculation on the system dynamics model to obtain a circulation relationship analysis result of the green evidence among the multiple markets. According to the embodiment of the invention, quantitative analysis can be carried out on the green evidence production and elimination balance and circulation relationship in a multi-market environment, and sustainable development of the green evidence market is promoted.

The invention discloses a focusing noise optimization method and device, a storage medium and a product, and relates to the technical field of interference noise optimization, and the focusing noise optimization method comprises the steps: obtaining an initial interference image containing multiple types of focusing noise, inputting the initial interference image into a preset noise error correlation model, and obtaining the noise influence coefficient of each type of focusing noise, the noise error correlation model is obtained by training the to-be-trained model based on an interference image sample, the interference image sample comprises multiple types of focusing noise and an actual focusing error, and the focusing noise of the corresponding type is removed through a corresponding denoising algorithm based on the noise influence coefficient. According to the method and the device, the to-be-trained model learns the relationship between various types of noise and the focusing error, so that the influence coefficient of various types of focusing noise on the focusing error can be determined. Based on the noise influence coefficient, the focusing noise of the corresponding type is removed in a targeted manner, the wavefront curvature extraction precision is prevented from being influenced, and the focusing precision of the wavefront curvature interference method is further improved.

The invention provides a multi-element load association prediction method and system, and the method comprises the steps: obtaining the real-time load data of each related load in a related load group of a target park in a first time period, the related load group being obtained through the division according to the initial influence coefficient between the loads of the target park, and the real-time load data of each related load in the first time period; the initial influence coefficient between the loads is obtained by performing correlation analysis on the loads; inputting each piece of real-time load data into a preset associated prediction model so as to enable the associated prediction model to generate single load prediction data of each related load in a second time period, and then adjusting each piece of single load prediction data according to an influence coefficient between the related loads, and generating load correlation prediction data of each related load in the second time period. According to the method, the mutual influence among the loads is fully considered in the load prediction process, and the accuracy of multi-element load prediction is improved.

The application discloses a key backlight correction method and device, electronic equipment and a readable storage medium, which are applied to the technical field of backlight correction and a key device, and the key device comprises at least one key and a key backlight lamp, and the key backlight correction method comprises the following steps: acquiring influence coefficients of the key backlight lamp on each key, wherein the influence coefficient is used for representing the influence degree of the key backlight lamp on the display brightness of each key; selecting at least one target key from each key according to the influence coefficients; and performing backlight correction on each key by adjusting the lamp display brightness of the key backlight lamp according to the preset key display brightness of each target key and the influence coefficients. The application solves the technical problem of large resource loss of key backlight.

This invention discloses a method for predicting turbine blade flutter. The key point is the coupling of a real physical model of unequilibrium wet steam with the influence coefficient method for predicting flutter in the last-stage long blades of a turbine. This involves solid modeling of the last-stage moving blade, modal analysis, and harmonic response analysis, and constructing a nine-channel fluid domain for influence coefficient method calculations. The extracted main mode shapes are then interpolated onto the CFD nodes of the nine-channel fluid domain using a three-dimensional linear interpolation method. Furthermore, by modifying the wet steam model to accurately characterize the two-phase flow characteristics of the last-stage wet steam, unsteady flow field calculations using the nine-channel influence coefficient method are performed to obtain the aerodynamic damping coefficients for different nodal diameters. This enables the prediction of the flutter stability of the last-stage moving blades and the identification of critical nodal diameters. This significantly reduces the time cost of recalculating the tip phase angle for each blade throughout the entire revolution and improves the physical reliability and reproducibility of the prediction.

The invention discloses a house settlement detection system and method based on multi-source data analysis, and relates to the field of house settlement detection. The method comprises the steps of obtaining settlement volume data and settlement influence factor data of a to-be-evaluated building at a t moment in a sampling period lambda at a fixed sampling frequency, preprocessing the obtained data, constructing a multivariable time sequence vector by the processed data, taking the settlement volume and settlement influence factors as endogenous variables of a VAR model, and obtaining a VAR model. Constructing a multivariable time sequence model by using the multivariable time sequence vector, determining an expression form of the settlement of the building to be evaluated at the t moment according to the constructed multivariable time sequence model, obtaining an influence coefficient of each settlement influence factor on the settlement in the expression form of the settlement, and determining the settlement of the building to be evaluated according to the obtained influence coefficient. Main influence factors causing settlement of the building to be evaluated at the t moment are determined, and prevention and control strategies and measures are formulated according to the main influence factors.

The invention relates to the technical field of load prediction, and provides an integrated energy system load prediction method and system based on multi-task migration, and the method comprises the steps: obtaining a load data set, carrying out the parameter updating of a basic load prediction model, calculating the influence coefficient of each model parameter, and migrating the basic load prediction model parameters to a new load prediction model; acquiring load data at the current moment in the operation process of the new integrated energy system, calculating an accumulative error at the current moment, and if the accumulative error is greater than a first threshold value, updating parameters of the new load prediction model according to the influence coefficient, and performing load prediction by adopting the new load prediction model; and putting the load data at the current moment into the new integrated energy system data set, calculating the distribution difference between the load data set and the new integrated energy system data set, if the distribution difference is greater than a second threshold value, adding the data in the new integrated energy system data set into the load data set, and updating the basic load prediction model parameters again. And the new load prediction model is ensured to learn the knowledge of the basic load prediction model.

This invention discloses a rock mass structural surface shear strength degradation model that simultaneously considers vibration abrasion and relative motion velocity. Addressing a significant shortcoming of current structural surface strength vibration degradation models—namely, the exaggeration of the influence coefficients of vibration abrasion and relative motion velocity on the vibration degradation of structural surface strength—this invention improves upon the aforementioned models based on the Barton criterion. It proposes a rock mass structural surface shear strength degradation model that simultaneously considers vibration abrasion and relative motion velocity. Furthermore, parameter sensitivity analysis is used to study the influence of peak ground acceleration, basic friction angle and roughness coefficient of the structural surface, and the shear strength degradation factor parameters m and P0 on slope seismic stability, providing a useful reference for slope seismic stability analysis.