120 results about "Weight factor" patented technology

The invention discloses a land space planning and fine management method based on big data, and relates to the technical field of land planning. A random forest model is adopted to dynamically adjust a weight factor, and factors such as land availability, ecological sensitivity and infrastructure bearing capacity are combined, so that the land space planning and fine management can be realized. The resource bearing capacity is comprehensively quantified, high, medium and low bearing capacity areas are divided, then ecological protection, industrial layout and public service coverage references are combined, an analytic hierarchy process (AHP) is adopted to calculate suitability scores of ecology, production and life, and accurate space division is realized through a rasterization method. Finally, the performability and adaptability of planning are enhanced through guided linear planning and a dynamic feedback mechanism, space conflicts are optimized through scene simulation, model parameters are continuously adjusted through remote sensing and IoT monitoring data, dynamic synchronization of planning and reality is achieved, and the problems that in traditional planning, static performance is high, and adjustment lags are effectively solved.

The invention discloses a method, a system and equipment for optimizing batching parameters in an industrial silicon smelting process. The method comprises the following steps: firstly, acquiring process parameters in an industrial silicon smelting process, standardizing the process parameters, then converting the process parameters into weight factors influencing a smelting result, and mapping a relation between the smelting result and the weight factors into a parameter equation based on a preset multiple linear regression model, obtaining a parameter set containing a weight factor and a linear regression coefficient; then, based on the parameter set, the initial raw material parameters and the reaction process parameters, an equivalent circuit model is constructed and simulated to generate smelting result data under different parameter combinations, and therefore parameter adjustment variables are obtained; according to the numerical range of the parameter adjustment variables and the initial raw material parameters, an optimal batching parameter combination is obtained; and finally, performing optimization treatment on batching parameters in the industrial silicon smelting process. According to the method, accurate control and rapid optimization adjustment of batching parameters can be stably achieved in the industrial silicon smelting process.

The invention discloses an underground pipeline intelligent detection and mapping method based on image recognition, and the method comprises the following steps: 1, obtaining continuous video images, associating feature matching pairs of adjacent key frames, and forming a pose parameter set; 2, inputting the key frame into an improved YOLO-World detection network, and outputting a detection result set; 3, obtaining a geometric consistency matching set according to the detection result set; 4, performing multi-view triangularization on the geometric consistent matching set to form a weight factor; 5, introducing a weight factor, and executing incremental beam adjustment optimization on the pose parameter set and the three-dimensional sparse point set to obtain a sparse semantic point cloud; and step 6, outputting an underground pipe network topology map. According to the invention, high-precision and high-robustness intelligent identification and topological mapping in a complex underground pipeline environment are realized.

The invention discloses an annotation task allocation method, a related device, equipment and a storage medium, and the method comprises the steps: obtaining the index data of a target task type executed by a candidate annotation customer based on the target task type of a to-be-allocated annotation task; wherein the index data comprises index values of a plurality of index dimensions; obtaining a weight factor of each index dimension under the target task type based on the configuration information about the relative emphasis of the plurality of index dimensions under the target task type; weighting based on the weight factor and the index value of the same index dimension under the target task type to obtain a marking quality score of the target task type executed by the candidate marking guest; and based on the annotation quality score of each candidate annotation customer, allocating a target annotation customer to each to-be-annotated file in the to-be-allocated annotation task. According to the scheme, the rationality of annotation task allocation can be improved.

The invention provides a medical material distribution method and device. The method comprises the following steps: counting historical state information of each department from bank scene financial data; based on a multi-output prediction strategy and the historical state information of each department, the number of predicted patients and a predicted severity value are obtained; for each department, setting a weight factor based on the predicted number of patients, and setting an importance factor based on the predicted severity value; constructing a scheduling model composed of an objective function and constraint conditions based on each weight factor, each importance factor and the existing material quantity; and solving the scheduling model to obtain a distribution result. In the scheme, the number of patients is predicted according to bank scene financial data, material weight factor estimation is carried out for different departments based on the predicted number of patients, priority division is carried out on the departments according to the urgent need degree, then refined modeling is carried out for a multi-material and cross-department scheduling model, the distribution reasonability is ensured, and the distribution efficiency is improved. The purposes of saving manpower and material resources and improving the doctor-seeing experience of the patient are achieved.

The invention relates to the technical field of tiny fault detection, and discloses an intelligent tiny fault detection method and system based on empowerment divergence, and the detection method comprises the steps: collecting offline production data at each moment in an industrial operation process; constructing a principal component analysis model and extracting a principal component; calculating a data score matrix; performing JS divergence test under a sliding window strategy; performing local outlier abnormal value detection on the sequence of the JS divergence statistics through a local outlier factor weighting method, and introducing an obtained local outlier weight factor into the JS divergence statistics; fitting data distribution through a smooth kernel function; calculating the weighted JS divergence statistic of the online production data, and judging whether a fault occurs or not; according to the method, JS divergence calculation of local outlier factor empowerment is introduced, and a principal component analysis technology is combined, so that an efficient and stable tiny fault detection model is constructed, and the detection capability of tiny faults under specific conditions or in a local area is improved.

The invention discloses a building full-life-cycle carbon effect quantification method considering time dynamics. The method comprises the following steps: step 1) building full-life-cycle staged dynamic consumption data collection; 2) dynamic list analysis; step 3) dynamic characterization; and 4) dynamic weighting and carbon effect quantification. The method has the beneficial effects that a plurality of variables dynamically changing along with time, such as family scale, personnel use behaviors, equipment replacement, waste disposal, energy structure, characterization factors and weight factors, of the residential building are fully considered, and the method for quantifying the dynamic carbon effect of the building in the full life cycle is developed; potential dynamic changes of the building in a long period can be brought into quantitative analysis, an operable dynamic evaluation normal form and a model method can be provided for carbon effect evaluation of the building, and carbon reduction and sustainable development of the residential building can be scientifically promoted.

The embodiment of the invention discloses a gas consumption demand prediction method, system and device and a storage medium. The method comprises the steps that a plurality of gas consumption demand prediction models are constructed; obtaining a fitting result of the gas consumption demand prediction values and the gas consumption actual values of the plurality of gas consumption demand prediction models; determining a weight factor of each gas consumption demand prediction model under each time node according to the fitting result; and predicting the gas demand by combining the plurality of gas demand prediction models with the weight factor of each gas demand prediction model at the current time node. According to the embodiment of the invention, the weight factor of each gas demand prediction value can be determined through the fitting result between the gas demand prediction values and the actual gas values of the plurality of gas demand prediction models, and the prediction result closer to the actual gas value can be predicted according to each gas demand prediction value and the weight factor thereof. Therefore, the effect of improving the prediction precision of the urban gas demand quantity is achieved.

The invention provides a high-resolution DOA (direction of arrival) estimation method and system, and the method comprises the steps: updating a model covariance matrix based on a to-be-corrected space power spectrum and noise information of the iteration of this round, thereby obtaining a direction dependence weight factor, and carrying out the estimation of the direction dependence weight factor. And according to the direction dependence weight factor of the current round of iteration, the to-be-corrected spatial power spectrum, the model covariance matrix and the sample covariance matrix, predicting the to-be-corrected spatial power spectrum and noise information of the next round of iteration, and finally obtaining the spatial power spectrum. According to the method, on the premise that the number of prior information sources is not needed, the problem of spectrum peak merging of a traditional sparse method under the condition of adjacent information sources is solved, stable separation of near-angle incoming waves is achieved, and the high-resolution performance of DOA estimation is remarkably improved.

The invention provides a landslide mass recognition method and device, equipment, a storage medium and a program product, and can be applied to the field of landslide mass recognition. The method comprises the following steps: based on a landslide mass identification model, respectively carrying out feature extraction on images from a plurality of modalities to obtain extraction features corresponding to the images; converting the extracted features into corresponding frequency domains and carrying out frequency domain compression to obtain frequency domain features corresponding to the image; converting the frequency domain features into weight factors, and obtaining weighted features corresponding to the images according to the weight factors and extraction features corresponding to the same image; splicing the weighted features of the images to obtain fusion features; processing the fused features through a multilayer dense network, and carrying out multiple interpolations on the processed features by using a bilinear interpolation method; and obtaining the category of each pixel in the features after multiple interpolations by using a maximum parameter value decision method, and determining the landslide mass.

The present invention relates to the technical field of ship inspection. Disclosed are a ship inspection system and method. The system comprises an inspection item database, an inspection task module, an inspection report generation module, an inspection analysis module and optimization modules, wherein the inspection task module is used for sequentially calling ship inspection items from the inspection item database and constructing a ship inspection plan on the basis of the ship inspection items; the inspection report generation module is used for performing inspection of the ship inspection items on the basis of the ship inspection plan to generate a ship inspection report; the inspection analysis module is used for analyzing the ship inspection report to obtain an analysis result of the ship inspection items, and calculating weight factors on the basis of the analysis result; and the optimization modules are used for adjusting the order of the ship inspection items in the inspection item database on the basis of the weight factors. The system can standardize ship inspection items, and associate ship inspection tasks with inspection reports.

The invention relates to an equivalent two-level fast double-vector model predictive control method applied to a three-level inverter. The method specifically comprises the following steps: sampling output current and direct current side upper and lower capacitor voltages at a k moment; predicting an expected voltage according to the mathematical model of the three-level inverter and the expected current; performing first value function calculation according to an equivalent two-level vector set composed of positive and negative small vectors to obtain an optimal small vector; calculating a second value function according to the equivalent two-level vector set where the optimal small vector is located to obtain a suboptimal vector; according to the characteristic that the vector action time is inversely proportional to a value function, the action time of an optimal small vector and the action time of a suboptimal vector are calculated; and applying a switching signal under the combined action of the optimal small vector and the suboptimal vector to a three-level inverter. According to the method, the vector set equivalent to two levels is established, so that the vector optimization process is simplified, and the model calculation time is greatly shortened; a value function of a single control target is established, and the tedious design process of weight factors is omitted; and a double-vector model predictive control method is adopted, so that output current harmonics are reduced.

A memory system and method for performing adaptive prefetch in a memory system are provided. The method includes receiving a first data request including a first address of a first data page stored in a memory device; and performing a lookup operation using an adaptive prefetch table, to determine whether a prefetch operation should be performed based on the received first data request. The adaptive prefetch table includes an entry for the first data page corresponding to the received first data request, and the entry for the first data page includes an address of a predicted next page, and a weight factor of the predicted next page.

The invention discloses a motor multi-objective optimization method and device based on a combined proxy model. The method comprises the following steps: training each meta-proxy model by adopting a one-leaving cross validation method; determining a weight factor of each element agent model based on the error correlation matrix, and combining the element agent models into an optimized combination model according to the weight factors of the element agent models; during optimization, processing the extracted data of each to-be-optimized variable by using the optimization combination model to obtain optimization target data, and performing Sobol sensitivity analysis on each to-be-optimized variable by using the optimization target data to obtain a first-order influence index corresponding to each to-be-optimized variable and each total effect index; on the basis of the first-order influence index and the total effect index, hierarchically dividing each to-be-optimized variable; optimizing each to-be-optimized variable layer by layer by using the combinatorial optimization model; and selecting a final optimization result of each to-be-optimized variable of the target motor from the optimization result of the last layer.

The invention discloses a unified identity generation method. The method comprises the following steps: acquiring a plurality of advertisement request events, wherein each advertisement request event comprises at least two mutually associated feature identifiers; according to the plurality of advertisement request events, determining a plurality of feature identifiers and association relationships among the plurality of feature identifiers, each association relationship having a corresponding event time; a connected subgraph is generated by taking a group of feature identifiers with the incidence relation in the plurality of feature identifiers as nodes and taking the incidence relation between the feature identifiers as edges, each edge of the connected subgraph has a corresponding weight factor, and the weight factor is attenuated along with the event time of the corresponding incidence relation; and generating a corresponding unified identity label based on the connected sub-graph.

The invention belongs to the technical field of electric valves, and provides a valve health state monitoring system and method.The method comprises the steps that a data processing module collects and preprocesses multi-dimensional parameters of an electric valve sealing piece, a gear and a carbon brush in real time by constructing a fusion sensor array, and a key component comprehensive monitoring data set is formed; the key component life module constructs a residual life prediction model of the carbon brush, the gear and the sealing element based on an accumulative damage theory and the data set; the similarity analysis module calculates an initial weight factor of a historical failure case, and calculates the similarity in combination with a residual life prediction result; the similar sample module screens similar samples to form a sample set so as to obtain an advanced weight factor set; a fitting weight factor screening module screens out a fitting weight factor based on the residual life similarity; the comprehensive prediction model construction module constructs an electric valve comprehensive prediction model by using the fitting weight factors; and the maintenance module performs prediction based on the model and gives maintenance suggestions.

The invention relates to the technical field of gear box state evaluation, and discloses a vehicle gear box state evaluation method, which comprises the following steps: S1, collecting physical and chemical performance data of oil in a gear box; s2, preprocessing the collected data through an independent component analysis (ICA) method, and screening out a data set which can be used as model input; dividing the model input data set into a test data set and a training data set; s3, improving a RBMO (Red Blue Mark Optimization) algorithm, including initializing an RBMO population through a Latin hypercube sampling method to improve the population initialization uniformity; a spiral search strategy is introduced in the RBMO foraging stage, so that the red-mouth blue-streak search efficiency is improved; in the RBMO attack stage, a weight factor is introduced to optimize the position update of the red-beaked blue-streak, and an improved red-beaked blue-streak optimization algorithm IRBMO and the like are obtained; according to the method, the defect of blindness of parameter selection in the training process is overcome, and the prediction precision of the regression prediction model is improved.

The invention relates to the technical field of civil aircraft automatic flight intelligent control, in particular to an intelligent control method and system in the flight process of a civil aircraft, and the method comprises the steps: collecting related data of the civil aircraft in the flight process of historical preset times; determining a vertical speed control stage of the civil aircraft in each historical flight process; each stage in the flight process is re-divided; constructing influence factors of various environments on the vertical speed; constructing a weight factor when the current flight process is predicted in each historical flight process, and predicting the target vertical speed in the current flight process of the civil aircraft; and adaptively adjusting the gain value through the deviation between the predicted target vertical speed and the actual vertical speed. The invention aims to improve the control efficiency and precision of the vertical speed in the flight process of the civil aircraft by analyzing the related data in the flight process of the civil aircraft.

The invention discloses a popularity evaluation method and device, equipment and a storage medium, and relates to the technical field of big data, and the method comprises the steps: obtaining current operation data of a current service, and constructing a current item set according to index items exceeding a corresponding index range in the current operation data; matching the current item set with the frequent item set, and determining at least one target association rule corresponding to the current item set in the strong association rules according to the matched target frequent item set; determining a popularity index of the current service according to the exceeding degree of each index item in the current item set and the rule attribute and the weight factor of each target association rule; and determining the current popularity level of the current service according to the popularity index and the index interval corresponding to each popularity level. According to the technical scheme, the popularity level of the service is determined according to the popularity index of the service, accurate judgment of the popularity level of the service is achieved, an accurate data basis is provided for operation and maintenance of a service system according to the popularity level, and the operation and maintenance effect is improved.

The invention relates to the technical field of big data, particularly provides a target trajectory and behavior analysis method and device based on a clustering algorithm, and a medium, and aims to solve the technical problem that the accuracy of abnormal behavior prediction of a target person by an existing method is relatively low. In order to achieve the purpose, the target trajectory and behavior analysis method based on the clustering algorithm comprises the steps that historical trajectory information of a target object is acquired; performing feature extraction on the historical track information of the target object to obtain a feature matrix; determining a weight factor corresponding to the historical track information; clustering the feature matrix based on the weight factor to obtain a clustering result; and predicting the abnormal behavior of the target object based on the clustering result. By adding the weight to the historical data from the space-time dimension, the defect of the historical data is effectively made up, the accuracy of the clustering result is improved, and thus the abnormal behavior of the user can be accurately predicted.

The invention discloses a binocular vision naked eye 3D image processing method, device and system, and particularly relates to the technical field of naked eye 3D. According to the method, the foreground and background areas are defined, a parameter extraction and merit figure quantification calculation system covering parallax, scenes and splicing dimensions is constructed, and the multi-scene preset threshold and the weight factor are combined, so that comprehensive and accurate evaluation of the naked eye 3D image quality is realized, the problem of one-sided evaluation of a traditional single index is solved, and the evaluation efficiency is improved. And the core crux influencing the three-dimensional effect is accurately positioned.

The invention relates to the technical field of measurement, in particular to a float-type water level gauge dynamic compensation detection method which comprises the following steps: collecting float angle sequence data; synchronously acquiring environment disturbance parameters; calculating the floater angle sequence data to obtain a preliminary water level value; constructing a dynamic compensation model, and outputting an inertia force correction term, a fluid damping correction term and a mechanical hysteresis correction term; the inertia force correction term, the fluid damping correction term and the mechanical hysteresis correction term are multiplied by corresponding weight factors respectively, then algebraic sum is solved, a total compensation amount is obtained, and the weight factors are dynamically adjusted and normalized according to the current wave high energy spectral density, the wind speed gradient change rate and the water temperature viscosity correction coefficient; and deducting the total compensation amount from the initial water level value, and outputting a final compensated water level value. According to the method, the problems of poor multi-physical field sensing capability and poor online parameter identification capability of a compensation method in the prior art are solved.

The invention discloses an electric vehicle charging load prediction method in a low data quality scene, and relates to the technical field of electric vehicles, and the method comprises the steps: obtaining a first feature; setting a dynamic space-time weight factor according to the first feature; according to the dynamic space-time weight factors, vacancy values in the space-time associated charging power matrix are complemented, and complete target charging power matrix data are obtained; constructing a three-dimensional input tensor according to the complete target charging power matrix data; inputting the three-dimensional input tensor into a first model to obtain a charging load predicted value; performing trend verification on the charging load prediction value and historical charging load data to obtain a prediction trend; and if the difference value between the prediction trend and the historical trend exceeds a preset threshold value, correcting the charging load prediction value based on the change rate of the historical trend to obtain a target electric vehicle charging load prediction result. According to the method, high-precision load prediction can be realized, and powerful support is provided for power grid demand response.

The invention discloses a tight oil and gas reservoir horizontal well volume fracturing fracture network wave and volume prediction method, which comprises the following steps of: based on geological engineering parameters of a fractured horizontal well and microseism monitoring fracture volume data, calculating weight factors of each geological and engineering parameter by establishing a multi-factor evaluation model; aiming at a to-be-predicted fracturing section, combining the solved weight factor and the geological parameters of the perforation positions of the fracturing clusters in the fracturing section, and calculating a fracturing crack-making efficiency coefficient representing the distribution uniformity of fracturing fluid; multiplying the original micro-seismic monitoring fracture volume of the fractured section by the calculated fracture forming efficiency coefficient to obtain a corrected effective fracture network wave and volume; according to the method, the fracture forming efficiency coefficient is introduced to correct the original microseismic monitoring volume, the problem that prediction results of an existing method are generally large is solved, accurate quantitative prediction of the effective fracture network waves and the volume is achieved, and a scientific basis is provided for fracturing effect evaluation and scheme optimization.

The invention discloses a two-parameter temperature control method for a quantum computing measurement and control system, which comprises the following steps of: firstly, determining a difference value between a current temperature and a target temperature, namely a current instantaneous temperature difference as a first parameter, calculating a change rate of the temperature difference in unit time in real time as a second parameter, and calculating to obtain a fan rotating speed compensation value through a formula =; k is a dynamic adjustment coefficient and is a weight factor of the change rate of the temperature difference in unit time. The invention further discloses a two-parameter temperature control system for the quantum computing measurement and control system. According to the method, a two-parameter coupling formula is provided, a proper fan rotating speed compensation value is calculated, self-adaptive accurate temperature control is achieved, the method is suitable for fan rotating speed adjustment under different environment temperature conditions, and the method has wide applicability.

The invention provides a student behavior prediction method based on multi-modal data fusion, and the method comprises the steps: predicting a missing value through a data entropy calculation formula if a reliability and integrity score is lower than a preset threshold value, and complementing a heterogeneous feature set through information uncertainty quantification, thereby obtaining a complemented feature set; extracting a feature mode through the complemented feature set, calculating the contribution degree of each data source type by adopting a weight distribution basis, and generating an integrated feature vector by dynamically adjusting a threshold value; if the contribution degree distribution does not meet the uniformity standard, adjusting the weight proportion according to the importance of the data source type, and generating an adjustment weight vector through entropy normalization processing and algorithm iterative loop; and if the confidence fusion rule displays a deviation mode, optimizing a scene adaptation index through weight factor updating and real-time state feedback, and determining the reliability of an evaluation result.

The invention discloses an offshore wind turbine generator stochastic dynamics analysis method capable of processing high-dimensional stochastic factors, which comprises the following steps of: establishing a self-adaptive weight factor-based uniform subdivision technology, and obtaining a high-dimensional stochastic input variable of a structure in a complex environment; deriving a probability density integral equation of the offshore wind turbine generator with high-dimensional random factors, and depicting a structural uncertainty propagation process; solving the probability density integral equation by adopting a direct probability integral method based on a uniform subdivision technology to obtain a probability density function of the structural response of the wind turbine generator; and in combination with equivalent extreme value mapping, a direct probability integral method based on a uniform subdivision technology is further used for solving the fatigue reliability of the wind turbine generator. According to the self-adaptive uniform subdivision technology based on the weight factors, the weight factor solving process and the representative point selecting process are decoupled, the representative points of the high-dimensional probability space can be generated in a self-adaptive mode, the assignment probability of the representative points is calculated based on the weight factors, and the random factors of the structure in the complex environment are obtained.

The invention belongs to the field of aircraft landing, and particularly relates to an aircraft automatic landing flight quality evaluation method, which divides a landing process and analyzes the influence of different landing stages on arresting landing, determines evaluation indexes of each stage, a decision point and a touch plate point, and then establishes a comprehensive evaluation system of the landing process. The method comprises the following steps of: firstly, obtaining a weight factor of an evaluation index system by utilizing an expert scoring method, and introducing different membership functions to score an evaluation index; and finally, obtaining a comprehensive evaluation score of the automatic landing process of the aircraft by combining the evaluation score of the index in each stage and the weight factor of each level. By evaluating the automatic landing process and constructing a comprehensive evaluation system, the systematicness and logicality of the evaluation method are improved; a weight factor is obtained by utilizing an expert scoring method, so that the trueness of an evaluation result is improved; and engineering realization is easy.

The invention discloses a pressure control method and system for a marine ammonia fuel supply system based on model-free self-adaptive control, and the method employs a model-free self-adaptive control scheme based on tight format dynamic linearization, and carries out the real-time collection of system pressure and valve control quantity data. And dynamically estimating a pseudo partial derivative phi (k) on line to capture system characteristics, and designing an adaptive control law to calculate a valve opening instruction according to the system characteristics. A dual-robust mechanism is formed by introducing an estimation weight factor mu and a control weight factor lambda, and noise interference and controlled quantity mutation are effectively suppressed. The method can automatically adapt to system dynamic change and external disturbance, high-precision and strong-robustness stable control over the ammonia fuel supply pressure is achieved under the complex ship working condition, and the safety and reliability of the ammonia power ship are remarkably improved.

The present invention discloses a side milling cutter position planning method for weight adjustment of non-developable ruled surfaces. The method first completes the non-developable ruled surface construction based on the original control points; secondly, the original two-point offset algorithm is used to obtain the initial tool axis vector with equal parameters, and the maximum overcut position of the corresponding side milling area is solved; then the maximum overcut offset position and the initial tool vector are used to form two segmented vectors, and the weight factor is calculated; finally, the side milling cutter axis vector after weight adjustment is obtained. In addition, the method calculates different weight factors for different distortion positions, which can be better adapted to the side milling of non-developable ruled surfaces. The present invention focuses on the side milling processing errors of non-developable ruled surfaces, especially the overcut and individual positioning tool errors, and completes the purpose of outputting the tool axis vector by the weight optimization algorithm with the initial tool axis vector as the starting point, thereby improving the side milling processing accuracy and efficiency, and providing certain technical support for the field of modern mechanical manufacturing and processing.