48 results about "Influence coefficient" patented technology

The invention relates to the technical field of data processing, and discloses an Internet of Things gateway data grading processing method and system based on edge computing, and the method comprises the steps: collecting original operation data and a data traffic value received by an Internet of Things gateway; performing feature extraction and clustering grouping according to the original operation data, calculating an average influence coefficient of samples in a group in combination with a preset service weight, and determining an importance level corresponding to the group; important data is screened out for distribution immediate evaluation; performing aging characteristic analysis according to the important data, and predicting a data processing priority; analyzing resource demand allocation according to the priority sequence and the data flow value, and constructing a resource allocation proportion; according to a resource allocation proportion and an allocation proportion lower limit, identifying nodes with insufficient proportions and optimizing the priority sequence to obtain a final scheduling scheme; according to the final scheduling scheme, the processing sequence is applied to the original operation data, and data grading output is obtained. According to the method, efficient and accurate data processing is realized.

The invention relates to the technical field of landform classification, and discloses a complex mountain landform intelligent classification method and system based on dual-scale TPI optimization, and the method comprises the steps: firstly obtaining ASTER GDEM data of a research area, dividing a landform analysis unit, and extracting initial landform factors (large / small scale landform position indexes, gradients and elevations); after standardization processing, calculating a Pearson correlation coefficient and a variance expansion factor to diagnose factor colinearity, and then determining an optimal terrain factor combination through screening calibration; then matching the data with historical landform classification data of a research area, judging whether to adjust input parameters of a machine learning model, if so, determining primary and target influence coefficients of the model, and optimizing hyper-parameters of a random forest, extreme gradient lifting and a deep neural network according to the primary and target influence coefficients; and finally, finishing the landform classification of the research area by utilizing the optimized model and outputting a result. By optimizing terrain factor combination and machine learning model parameters, the accuracy and efficiency of landform classification of the research area are improved.

The invention discloses a visual monitoring service system and method based on product detection, and relates to the technical field of abnormal loss monitoring. Historical data of image records used by reagents in a laboratory are collected, and the types of the reagents in the image records and the actions of using the reagents by operators are marked; calculating the duration time of each action type in the action sequence of the reference time period and the occurrence frequency of two adjacent action combinations to obtain the unit time threshold value of the action type and the frequency threshold value of the action combination, and respectively obtaining the difference between the unit time threshold value and the frequency threshold value; and calculating an influence coefficient of the action duration time difference and the occurrence frequency difference on the abnormal consumption amount of the reagent, identifying the duration time of the action and the occurrence frequency of the action combination in a unit time period, and predicting the abnormal consumption amount of the reagent in the unit time period in combination with the influence coefficient.

The invention discloses an automatic ship collision avoidance decision-making system, and relates to the technical field of ships, and the technical scheme is characterized in that the method comprises the steps: collecting environment data of a target ship in a navigation region and target ship dynamic data of the target ship in a navigation process; dividing the navigation area into navigation area blocks according to the risk level and the environmental data, and performing feature extraction on the dynamic data of the target ship to obtain dynamic feature parameters; analyzing and determining the normal variation range of the influence coefficients of the navigation region blocks with different risk levels to obtain a reference interval value; according to the reference interval value, performing statistics on influence coefficients between the current target ship and the surrounding environment and between the current target ship and other ships in the sailing process to obtain a statistical result, and comparing the statistical result with the reference interval value to obtain a collision avoidance risk early warning or continuous analysis instruction; the method has the effects of reducing the occurrence rate of collision accidents and improving the safety and orderliness of marine navigation.

The invention provides a special steel forging process microstructure intelligent prediction method based on machine learning, and relates to the technical field of microstructures. Comprising the steps of collecting process parameter data, microstructure data and dislocation motion data. And obtaining an influence coefficient according to the influence degree of the dislocation motion data on the dynamic recrystallization behavior, processing the microstructure data according to the influence coefficient to obtain optimized dynamic recrystallization behavior data, and adjusting the process parameter data according to the optimized dynamic recrystallization behavior data to obtain structure type data. And for the optimized generalized regression neural network model, inputting tissue type data, and outputting to obtain a microstructure prediction result. According to the method, the regression neural network model is optimized by using the fruit fly optimization algorithm, the microstructure prediction result can be accurately output, the influence of the dynamic recrystallization behavior on microstructure formation can be predicted, better microstructure and performance can be obtained, and the production risk is reduced for special steel forging production.

The application discloses an intelligent resource scheduling method and system based on a dynamic data blood relationship graph, and relates to the technical field of resource scheduling.The method comprises the following steps: collecting the execution log and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted blood relationship graph; calculating the blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship graph; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship graph of the to-be-scheduled task, the real-time load state of a target node and the scheduling execution time delay; performing priority sorting on the to-be-scheduled task based on the comprehensive priority score, and allocating cluster resources; and predicting the load trend of the target node, and triggering a migration decision when it is detected that the predicted load of the target node exceeds a threshold value and the key blood relationship task weight proportion borne by the target node exceeds a preset threshold value. Through the dynamic blood relationship graph and the intelligent scheduling algorithm, the efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and the task delay is reduced.

The present invention discloses a method for characterizing the comprehensive performance of a hob, including the selection of hob performance characteristic parameters, the determination of performance characteristic levels, the calculation of parameter influence coefficients, the determination of performance characteristic functions, the comprehensive performance characterization results, and the selection of hob types; the comprehensive performance of the hob is characterized by performance characteristic parameters such as hardness, load-bearing capacity, cutter ring wear resistance, impact resistance / cutter ring toughness; the performance characteristic levels of the hob are characterized by five levels: level I, level II, level III, level IV, and level V; the influence coefficients of different characteristic parameters of the hob in hard rock formations, soft rock formations, and formations with uneven hardness are established by using analytic hierarchy process and expert evaluation; the comprehensive performance characteristic function of the hob is constructed by using trapezoidal membership function or triangular membership function. The present invention can qualitatively and quantitatively characterize the comprehensive performance of the hob, sort and evaluate the comprehensive performance of the hob according to the characterization results, and optimize the type of hob most suitable for the actual engineering project.

The invention discloses a subway vibration response layered medium domain coupling analysis method based on a time domain boundary element method, and the method comprises the steps: building a boundary node coordinate matrix, an internal node coordinate matrix and a unit positioning matrix of each sub-domain based on the material parameters of a soil layer, subway vibration load numerical simulation data and load action time; generating a common boundary node relation matrix based on the boundary node coordinate matrix and a common boundary condition; calculating a unit influence coefficient matrix of each sub-domain based on the unit positioning matrix and the material parameters, and correcting the unit influence coefficient matrix of each sub-domain to obtain a corrected unit influence coefficient matrix of each sub-domain; based on the corrected influence coefficient matrix of each sub-domain unit, assembling to form an overall influence coefficient matrix; based on the common boundary node relation matrix and the overall influence coefficient matrix, displacement and surface force of boundary points are obtained through solving; and calculating displacement and stress of the internal nodes based on the internal node coordinate matrix and the internal node influence coefficient matrix.

The present application belongs to the technical field of data processing, and particularly relates to a reinforced concrete support construction quality detection method based on data processing. The method comprises the following steps: acquiring raw material proportioning, mixing, pouring and vibrating, and curing environment multi-source real-time data of the whole construction process; performing improved DBSCAN clustering preprocessing (missing value filling, outlier elimination, and standardization); constructing a dynamic correlation model by using a neural network, and optimizing hyperparameters by using an improved starling optimization algorithm; calculating influence coefficients and quality correlation degrees of each link; determining the quality by comparing the quality correlation degrees with a threshold value, and iteratively updating the model until the construction is completed. The present application realizes real-time detection throughout the whole cycle, reduces manual deviation, avoids quality defect solidification, reduces repair cost, and improves the reliability of support structure construction.

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 invention discloses a quantitative analysis method for the impact characteristics of a forging hammer based on a pulse function. The invention includes: obtaining the formula for the average impact force, as follows: ; obtaining the integral formula for the average impact force based on the unit pulse function and the formula for the average impact force; obtaining the expression for the peak impact force based on the pulse peak factor, rebound coefficient, and influence coefficient, as follows: , where is the peak impact force, is the influence coefficient, is the pulse peak factor, is the hammer mass, is the hammer impact velocity, is the mass ratio, is the reciprocal of the mass ratio, and is the base mass, which includes the mass of the equipment and the foundation. This invention solves the problem of large calculation errors in the impact force of forging hammers in related technologies.

The invention discloses a water supply strategy dynamic optimization method and system based on multi-source water affair data fusion. The method comprises the following steps: dividing a water supply area into a plurality of target areas; influence coefficients are preset among the target areas; each target area comprises a plurality of sub-areas; acquiring multi-source water affair data of each sub-region; according to the multi-source water affair data of the sub-regions, obtaining predicted water consumption data; based on the predicted water consumption data of each sub-region in the target region, obtaining total water consumption data corresponding to the target region; and obtaining a water supply strategy corresponding to each target area based on the influence coefficient between the target areas and the total water consumption data. The technical problems that in the prior art, the accuracy of the water supply scheduling process is low, and response to water demand changes is not timely are solved.

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.

The present invention discloses a method, system and storage medium for optimizing the slab type of precast floor slabs based on the principal component method. The method includes: obtaining the plane division results of each basic unit of the target precast floor slab, determining the unit area of each basic unit and calculating the fractal dimension index of each basic unit; performing standardization processing on the unit area and fractal dimension index of each basic unit; constructing a covariance matrix and calculating the variance of the data group after standardization of the unit area and fractal dimension index of each basic unit; calculating the principal component weights of the two dimensions of the unit area and fractal dimension index through the variance of the data group; calculating the influence coefficient of each basic unit, and sorting according to the level of the influence coefficient of each basic unit, and selecting the slab type through the order. The present invention calculates the influence level of each basic unit in multiple dimensions by constructing a covariance matrix, so as to realize the modular coordination selection of precast slab types, improve the use efficiency of materials and the stability of the structure.

The invention provides a micro-service capacity expansion and contraction and deployment method and device. The method comprises the following steps: constructing a time sequence directed acyclic graph based on time sequence micro-service data; performing spatio-temporal feature extraction on the features of each node and each directed edge in the time sequence directed acyclic graph to obtain a spatio-temporal feature set sequence, and outputting an influence coefficient result for representing a cross-layer load propagation relationship based on the spatio-temporal feature set sequence; and determining a target capacity expansion and contraction and deployment strategy of each micro-service based on the enhanced state by adopting a reinforcement learning model, and executing copy capacity expansion and contraction and deployment operation of each micro-service according to each target capacity expansion and contraction and deployment strategy. According to the invention, the joint decision of micro-service automatic capacity expansion and contraction and deployment can be dynamically realized to reduce service response time and accelerate QoS recovery as optimization objectives.

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.

The invention relates to the technical field of smart power grids, and discloses a multi-factor associated return winter power load pre-control method and system, and the method comprises the steps: converting the pre-obtained historical power consumption data to obtain power load data, and recognizing the power load data to obtain a power consumption problem; analyzing the power consumption problem to obtain influence factors, analyzing the influence factors to obtain a strong correlation factor data matrix, and analyzing the strong correlation factor data matrix to obtain correlation influence coefficients; performing iterative training on the power load prediction model to obtain a trained power load prediction model, and inputting pre-acquired current power consumption data into the trained power load prediction model to obtain load prediction data; dividing the power grid to obtain red, yellow and green grading transformer areas; and a power load pre-control scheme is formulated, so that power utilization prevention and control treatment is carried out. The method can solve the problems that the power grid overload is difficult to prevent, control and treat and the power consumption prediction is not accurate enough in the return winter.

The present application discloses a problem-solving hint method, device, equipment, and storage medium; wherein, the method includes: determining a target question to be hinted; determining an influence coefficient of a test point involved in the target question; wherein, the influence coefficient represents the influence degree of the corresponding test point on the user's correct solution of the target question; determining a first target test point whose influence coefficient meets the condition; and performing a problem-solving hint according to the first target test point.

The invention belongs to the technical field of waterworks water treatment, and particularly relates to a water plant intelligent dosing cloud control method, which comprises the following steps: S1, a data acquisition module acquires flocculation basin operation data, sends the data to a data communication module, and then sends the data to a cloud database; s2, the data processing module extracts the operation data for calculation, and judges whether the difference value between the water inlet flow and the water outlet flow is within a preset threshold value or not; if yes, determining the data as normal data; if not, marking the data as abnormal data; s3, calculating the change value of the normal data water inlet flow to obtain an influence coefficient, and then calculating the dosage of the medicament; s4, the data processing module sends an instruction to the control module, and the control equipment adds the medicament according to the medicament adding amount; and S5, the data processing module sends the abnormal data to a data statistics module, and fault early warning is generated. According to the method, the problems of inaccurate manual judgment, untimely response and limited processing capacity of an edge calculation mode can be solved, and the method has a relatively good market application prospect.

The invention relates to the field of data analysis, in particular to a data analysis method for mine ecological restoration, which comprises the following steps: acquiring monitoring data of acquisition points in a mine area, and taking the acquisition points as nodes in a graph network; calculating a space influence coefficient representing physical association strength among the nodes and a time effectiveness weight representing data timeliness; the construction method comprises the steps that weighted fusion is carried out on the cumulative sum of effective observation values of the nodes and effective observation values of neighborhood nodes, the effective observation values of the nodes are the products of the newest observation values of the nodes and the corresponding time effectiveness weights, and the sum of the effective observation values of the nodes and the cumulative sum of the effective observation values of the neighborhood nodes is equal to the sum of the newest observation values of the nodes. The effective observation value of the neighborhood node is the product of the effective observation value of the neighborhood node and the corresponding space influence coefficient; and predicting the health state of the mine based on the spatio-temporal spectrum aggregation state vector. According to the invention, accurate prediction of the health state of the mine is realized.

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.

The invention provides a violation evaluation method and device based on an entropy weight method and electronic equipment, and relates to the technical field of violation evaluation, and the method comprises the steps: obtaining a score deduction coefficient, and obtaining a score deduction matrix; obtaining the number of target individuals violating the question, and obtaining a violating people number matrix; obtaining an influence coefficient to obtain an influence matrix; according to the score deduction matrix, the violating people number matrix and the influence matrix, first evaluation scores of all stages corresponding to the first-level evaluation indexes of the multiple target groups are determined; on the basis of the multiple first evaluation scores, second evaluation scores of all stages corresponding to the multiple target groups are determined through an entropy weight method; determining target evaluation scores corresponding to the plurality of target groups according to the second evaluation score; and sorting the plurality of target groups according to the target evaluation scores to obtain a comprehensive evaluation result, so that the first-level evaluation indexes of the target groups can be quantified, the real conditions of the target groups can be reflected more accurately, and the multi-stage evaluation requirements of the target groups can be met.

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 belongs to the technical field of data processing, and particularly relates to a reinforced concrete support construction quality detection method based on data processing. The method comprises the following steps: acquiring multi-source real-time data of raw material ratio, stirring, pouring vibration and maintenance environment of the whole construction process; carrying out improved DBSCAN clustering preprocessing (missing value filling, abnormal value elimination and standardization); constructing a dynamic correlation model by using a neural network, and optimizing hyper-parameters by using an improved star-sparrow optimization algorithm; calculating the influence coefficient and the quality correlation degree of each link; the quality is judged by comparing the quality correlation degree with a threshold value, and the model is iteratively updated until construction is finished. Full-period real-time detection is achieved, manual deviation is reduced, quality defect solidification is avoided, the repair cost is reduced, and the construction reliability of the supporting structure is improved.

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.

The disclosure provides a resource configuration method and device, electronic equipment and computer readable storage medium, which can be applied to the fields of computer technology, data processing technology and logistics technology. The resource configuration method comprises: in response to receiving a resource configuration request, obtaining to-be-processed information and historical order information according to a current system time indicated by the resource configuration request; the to-be-processed information comprises current to-be-processed information corresponding to a current time period and historical to-be-processed information corresponding to a first historical time period, and the current time period and the first historical time period are determined according to the current system time; determining M influence coefficients and influence variables corresponding to the M influence coefficients according to the current to-be-processed information and the historical to-be-processed information; determining predicted order information according to the historical order information, the M influence coefficients and the influence variables corresponding to the M influence coefficients; and determining resource configuration information corresponding to a resource type to be configured according to the predicted order information.

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 present disclosure relates to a method, device, computer equipment and storage medium for determining an influence coefficient. The method for determining an influence coefficient includes: obtaining a first parameter value of a first preset parameter and a second parameter value of a second preset parameter, and determining a third parameter value of a third preset parameter based on the first parameter value and the second parameter value; in response to the first parameter value, the second parameter value and the third parameter value being suitable for fitting a first linear regression model, determining a target fitting result based on the fitting result of the first linear regression model for the first parameter value, the second parameter value and the third parameter value; and determining the influence coefficient of the first preset parameter and the second preset parameter on the third preset parameter based on the target fitting result. This method can intuitively see the degree of influence of the random deviation introduced by the process manufacturing and the process angle deviation between different wafers on the integrated circuit chip of the same components on the same wafer area, so as to ensure that the designed circuit can still meet the performance requirements after tape-out.

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.