44 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 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 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 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 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.

The application discloses a kind of docking station material loading scheduling method, device and storage medium, it is related to intelligent scheduling technical field, the method comprises: when target docking station needs to load, reference information is obtained, the reference information includes: the distance information between each material buffer bit and the target docking station, the storage remaining time of material in each material buffer bit and the remaining loading time of the next loading of the docking station to which each material buffer bit belongs;Obtain the weight factor of various reference information;According to the reference information and the weight factor of various reference information, the cost value of each material buffer bit is calculated;According to the cost value of each material buffer bit calculated, material is scheduled to the target docking station.The problem that the production efficiency is low due to the unreasonable scheduling rule in the prior art is solved, the time factor and the distance factor are considered simultaneously, the rationality of the scheduling rule is improved, and the production efficiency is improved.

The invention provides a population spatialization estimation method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: carrying out the functional region division of a target city region, and recognizing and extracting a residential land region; taking communities in the residential land area as modeling units, and combining night light intensity and the number of community residents to construct a random forest regression model of a community scale for estimating the population number of each community; and by taking the building volume of each residential building in the residential land area as a weight factor, allocating the population number of the community to each residential building in the community according to a proportion, and generating a building-level population distribution map. On the premise of not depending on high cost or sensitive data, building scale high-precision population space expression is achieved.

The invention provides an automatic testing method, device and equipment. The method is applied to automatic testing technology. The method comprises the following steps: in response to a test event, obtaining a weight configuration template of a current test round and a weight factor value corresponding to each test case in a plurality of test cases; and based on the weight factor value corresponding to each test case and the corresponding weight coefficient in the weight configuration template, determining the comprehensive weight of each test case. And according to the comprehensive weight, selecting a preset number of test cases from the plurality of test cases, and assigning the test cases to idle test resources for execution to obtain a test result of the current test round. And based on the test result, updating the weight factor value for the automatic test of the next test round. The problems that a traditional automatic testing method is static in testing strategy, single in evaluation dimension and lack of a real-time feedback mechanism can be solved.

The shale oil horizontal well volume fracturing segmented productivity prediction method of the present application has the following steps: obtaining the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period of each fracturing section of the shale oil horizontal well in the same block and the same development layer system in the field; using the analytic hierarchy process to establish a multi-level evaluation system of the shale oil horizontal well volume fracturing productivity influencing factors; establishing a productivity influencing factor evaluation model, calculating the weight factors of different influencing factors and sorting them; coupling the productivity influencing factors, establishing a productivity prediction model, calculating the horizontal well known liquid production profile test section and the prediction section similarity factor, and dynamically predicting the productivity of the prediction section through the known liquid production profile dynamic test result. The productivity prediction method of the present application couples the reservoir capacity, the fracture network complexity and the volume fracturing reconstruction strength factors, establishes a productivity prediction model, quickly and quantitatively predicts the liquid production profile, is accurate and reliable, and is also applicable to the similar oil reservoir horizontal well volume fracturing liquid production profile prediction.

The invention relates to the technical field of environmental governance project supervision and prediction, and discloses a soil acidification governance effect quantitative evaluation decision-making method and system, and the method comprises the steps: obtaining a system state variable sequence and an external disturbance intensity parameter of a governance project discrete node, by calculating a state variable transition rate and utilizing a steady-state weight factor to hedge extrinsic fluctuations, an intrinsic loss coefficient of a governance object stress-resistant buffer system is extracted, an efficiency attenuation prediction data chain is deduced to calculate a risk time margin, an intervention priority mapping relation is established, a governance resource reset direction scheduling matrix is updated, and the risk time margin of the governance object stress-resistant buffer system is calculated. According to the method, a calculation mechanism for stripping environmental noise from discrete sampling data is established, the recessive efficiency degradation trend of a governed land parcel is identified, and the problem of resource mismatching in administrative decision making is effectively solved.