68 results about "Weight factor" patented technology

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 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 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 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 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 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.

The application discloses a kind of compact oil and gas reservoir horizontal well volume fracturing fracture network swept volume prediction method, including, based on the geology engineering parameter of implemented fracturing horizontal well and microseismic monitoring fracture volume data, the weight factor of each geology and engineering parameter is calculated by establishing multi-factor evaluation model;For the fracturing section to be predicted, the weight factor obtained is combined with the geology parameters of each fracturing cluster perforation position in the fracturing section, and the fracturing fracture efficiency coefficient for characterizing fracturing fluid distribution uniformity is calculated;The original microseismic monitoring fracture volume of the fracturing section is multiplied by the calculated fracturing fracture efficiency coefficient, and the corrected effective fracture network swept volume is obtained;The original microseismic monitoring volume is corrected by introducing the fracture efficiency coefficient in the application, the problem that the prediction result is generally large in the prior art is overcome, and accurate quantitative prediction of effective fracture network swept volume is realized, which provides a scientific basis for fracturing effect evaluation and scheme optimization.

The present application relates to the technical field of measurement, and more particularly to a dynamic compensation detection method for a float type water level meter, which comprises: collecting float angle sequence data; synchronously collecting environmental disturbance parameters; performing calculation on the float angle sequence data to obtain a preliminary water level value; constructing a dynamic compensation model to output an inertial force correction term, a fluid damping correction term and a mechanical hysteresis correction term; multiplying the inertial force correction term, the fluid damping correction term and the mechanical hysteresis correction term by corresponding weight factors respectively, and then obtaining an algebraic sum of the three terms to obtain a total compensation amount, wherein the weight factors are dynamically adjusted according to the current wave height energy spectrum density, the wind speed gradient change rate and the water temperature viscosity correction coefficient, and are subjected to normalization processing; and deducting the total compensation amount from the preliminary water level value to output a final compensated water level value. The present application solves the problems of poor multi-physical field sensing ability and poor online parameter identification ability of the compensation method in the prior art.

The invention provides a wheel wear detection method and system. The method comprises the following steps: calculating a measurement parameter value of wheel tread measurement data; determining a control point coordinate and a curve weight factor according to the measurement parameter value, and constructing a tread curve after wheel abrasion according to the control point coordinate and the curve weight factor; calculating a difference area of a difference region between the tread curve after wheel abrasion and the wheel tread measurement data, and performing iterative optimization on a curve weight factor according to the difference area to obtain a weight factor combination; and obtaining the operation mileage of the to-be-tested train wheel, and determining the wheel abrasion value according to the operation mileage and the weight factor combination. According to the embodiment of the invention, the quantitative mapping relation between the abrasion characteristic parameter and the wheel abrasion value is constructed by taking the weight factor combination as the abrasion characteristic parameter, so that the corresponding wheel abrasion value can be automatically calculated after the operation mileage of the train wheel to be measured is obtained, and the specific condition of wheel abrasion can be intuitively reflected.

The application discloses a novel traffic scene target detection evaluation method and system, and relates to the technical field of target detection model performance evaluation. The application obtains the prediction result of a target detection model, determines true positive TP or false positive FP based on the matching relationship between the prediction frame and the real label, and respectively calculates the position weight factor and the scale weight factor. Then, the application obtains PSTP / PSFP by weighting TP / FP, constructs a position-scale weighted PR curve, and integrates to obtain the position-scale weighted average precision PSAP. The application can significantly improve the evaluation sensitivity of edge regions and small-scale high-risk targets, support scene adaptation and security level linkage, and make the evaluation results more suitable for the actual safety needs of automatic driving.

To provide a weighting factor determination device, a weighting factor determination method and a program, that can set a weighting factor of an evaluation function used for solving an optimization problem to a value suitable for the optimization problem.SOLUTION: A weighting factor determination device 200 comprises: calculating a calculated value di'_sim when a value of an evaluation function J is maximum or minimum for variables in an evaluation function that define an evaluation index ei' included in the evaluation function J by algorithm of a first optimization problem; calculating the value of a difference evaluation index |di'_sim-di'_opt| for evaluating the difference between the calculated value di'_sim of the variable in the evaluation function and a preset optimal value di'_opt of the variable in the evaluation function; and determining an optimal solution wi_opt for the weighting factor based on a calculated difference evaluation index value.SELECTED DRAWING: Figure 2

The present application provides a kind of geologic multi-source constraint adaptive full waveform inversion method and system, constructs multiple complementary constraint norms, organically fuses multiple modal prior information, forms unified multi-source constraint framework, compared with traditional single type prior constraint, the present application can more comprehensively utilize available prior information, guide inversion to the direction that both conforms to geophysical data and geological understanding converges;While introducing adaptive regularization parameter strategy based on Bayes theory, realize the adaptive update of weight factor, avoid the subjectivity and randomness caused by the dependence of regularization parameter on experience selection in traditional method, make the inversion result more stable, and do not need tedious manual parameter adjustment, can dynamically adjust the constraint strength according to the data noise level, enhance the guiding role of prior constraint in low signal-to-noise ratio, more rely on observation data in high signal-to-noise ratio;At the same time, through the combination of model compression and forward simulation calculation, the calculation efficiency is greatly improved.

The invention provides an analysis, early warning and release system based on a flood inundation condition, and relates to the technical field of flood early warning, the system comprises a constant flow special monitoring module, a probability forecast analysis module and a flood discharge risk analysis module, and the constant flow special monitoring module collects river data and rainfall data; judging whether flood analysis is started or not through risk index calculation; the probability forecast analysis module determines a weight factor based on historical data of each prediction model, obtains water level prediction data in combination with real-time water surface change data, and calculates a water area risk index to realize flood early warning; the flood discharge risk analysis module evaluates a flood discharge point use index according to historical flood discharge data, sets a monitoring point, calculates a flood discharge demand index by combining a water level, embankment stress and the like, and performs early warning on a risk flood discharge point, the system has multi-module cooperation and multi-dimensional analysis, flood risk early warning and flood discharge decision can be accurately realized, and the flood disaster prevention and control capability is effectively improved.

This invention relates to the field of image data processing technology, and more particularly to a method and system for processing store sales data. The method includes: acquiring reference samples and actual sales figures for each store, as well as samples to be predicted for which store sales figures need to be predicted; training a random forest algorithm and using the trained model for prediction; designating any prediction tree in the trained model as the target tree and determining the accuracy of the target tree; determining the weight factors for the samples to be predicted when using the target tree for prediction; and determining the final predicted sales figures for the samples to be predicted, thereby achieving store sales data processing. This invention dynamically allocates weight factors for each tree by comprehensively considering multiple dimensions such as the training sample coverage, prediction error of unselected samples, and sample path homogeneity of a single decision tree, avoiding interference from low-quality predictions on the final result and improving the accuracy and reliability of store sales prediction.

The embodiment of the invention provides a delivery duration prediction method, a computer program product and equipment. When the delivery duration of a target order is predicted, a weight factor can be determined based on delivery duration related data related to the delivery duration in order information of the target order, a first delivery duration can be determined based on the weight factor and a supply-demand ratio factor, and a second delivery duration can be determined based on the weight factor. And the distribution duration variation caused by the supply-demand ratio factor can be determined based on the difference between the first distribution duration and the second distribution duration, so that the variation can be utilized to assist operation decision making, profit sharing and the like. According to the method provided by the embodiment of the invention, the accuracy of the predicted delivery duration can be improved, and the influence of the supply-demand ratio factor on the delivery duration can be determined based on the difference between the delivery duration which considers the supply-demand ratio factor and the delivery duration which does not consider the supply-demand ratio factor by predicting the delivery duration which considers the supply-demand ratio factor and the delivery duration which does not consider the supply-demand ratio factor, so that an operation decision is assisted based on the influence.

The invention belongs to the field of electric power system dispatching automation, and discloses an electric power spot market electricity price similar day screening method based on a variable weight factor and a related device. The method comprises the following steps: acquiring historical power data, and constructing a single-day power spot market feature data set; obtaining electric power data of a prediction day, and constructing a similar day screening key feature set according to the electric power data of the prediction day and the single-day electric power spot market feature data set; obtaining a weight factor set of the seasonal characteristics corresponding to the prediction day; and screening the key feature set based on the weight factor set and the similar days, calculating a daily similarity comprehensive coefficient, and screening the obtained similar days based on the daily similarity comprehensive coefficient. According to the method, time period change is considered, seasons of different prediction days correspond to weight factor sets of different seasonal characteristics, the technical problem that an existing similar day screening method cannot adapt to the time period change and cannot capture a multi-bit characteristic dynamic coupling relation can be solved, and more scientific guidance is provided for follow-up electric power spot market operation decision optimization.

The invention relates to the technical field of machining and intelligent manufacturing, and discloses a heat treatment center hole quality detection method and system for a radial drilling machine machining shaft, and the method comprises the steps: S1, obtaining the processing data of the shaft on a radial drilling machine, and carrying out the cleaning; s2, extracting multi-dimensional features of the cleaned data to generate a quality feature vector; s3, inputting the feature vectors into a quality scoring model to output a comprehensive score and a risk coefficient, and dynamically calculating the scoring model by adopting a decision tree algorithm in combination with a weight factor; and S4, executing response operation by the quality control engine according to the scoring risk mapping rule. The system comprises a data acquisition module, a feature extraction module, a scoring engine, a quality control engine, a context sensing module and a response execution module. According to the method, the quality risk of subsequent processing is reduced, the defect identification accuracy is improved, and the production efficiency and the product percent of pass are improved.

The invention provides a dynamic ad hoc network positioning method and system based on cooperation of multiple unmanned aerial vehicles, and belongs to the technical field of unmanned aerial vehicle communication and localization, and the method comprises the steps: firstly obtaining an arrival angle spectrum and bit error rate data of a communication link, and correcting a spatial covariance matrix through employing a weight factor mapped by the bit error rate; then, determining a relative azimuth vector through characteristic decomposition and orthogonal projection, and constructing an observation matrix; and then mapping the observation matrix to a geometric configuration constraint space to establish a topological cost function, and utilizing gradient iteration to update and solve a target configuration. And finally, completing coordinate conversion in combination with the reference position information to realize global positioning of the unmanned aerial vehicle cluster. The method can effectively improve the precision of unmanned aerial vehicle cluster cooperative positioning in a dynamic environment by introducing bit error rate weighted correction angle estimation and combining geometric manifold constraint optimization.

The invention discloses a method for solving a partial differential equation by a physical neural network of a time evolution residual, and belongs to the field of solving the partial differential equation by the neural network. The method comprises the following steps that on the basis of a physical information neural network, a weight factor is introduced into an equation constraint term in a loss function, and the factor dynamically changes along with the training process; a problem domain is divided into a plurality of areas through a time axis in the training process, and sub-domain training is sequentially carried out backwards in the direction of the time axis; the unsteady problem is better solved by using the mode of sequentially activating and solving the physical domain. According to the method, the network is designed through dynamic evolution of the time domain, and the problem that a traditional physical information neural network cannot effectively solve unsteady partial differential is solved.

The invention provides a soil pollution accumulation prediction method based on multi-factor dynamic driving, and the method comprises the steps: analyzing the driving capability of each potential driving factor for the accumulation characteristics of soil pollutants based on a geographic detector method, and recognizing a main driving factor and a factor combination with the cooperative enhancement driving capability; inputting the identified main driving factors into a BP neural network model for training, determining a weight coefficient and a threshold value between an input layer and an output layer through iterative learning, and establishing a pollutant accumulation prediction model; in combination with historical driving factor data and carbon neutralization policy scene parameters, correcting parameters in the prediction model and generating driving factor data of a year to be predicted; and performing time sequence processing on the driving factor data by using a sliding window analysis method, dynamically adjusting the weight coefficient and contribution rate of each factor, and identifying future main driving factors.

This invention discloses a method and system for identifying transmission faults, comprising: simulating and determining transmission measurement points; acquiring vibration acceleration signals from the measurement points; extracting features from the vibration acceleration signals to obtain time-domain and frequency-domain indices, and then calculating the transmission's energy proportion characteristic parameters and the order characteristic parameters of each gear pair, thereby obtaining the transmission's characteristic parameter matrix and sensitive characteristic parameters; obtaining the transmission's comprehensive coefficients and defining faulty transmissions; constructing a weight factor matrix based on the transmission's sensitive characteristic parameters and characteristic parameter weight factors; using the normalized weight factor matrix of the identified faulty transmission types as a benchmark, calculating the PSI values ​​of other faulty transmissions; and classifying faults based on the PSI values ​​of the faulty transmissions. This invention, in judging transmission faults, does not rely on the experience of technical personnel, and has advantages such as unified evaluation standards, high efficiency, and easy problem tracing.