59results about How to "Reduce dimensionality" patented technology

The invention discloses an oil reservoir history fitting method based on graph lasso and set smooth multi-data assimilation, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, acquiring to-be-optimized model parameters and observation data of a target oil reservoir, constructing an observation error covariance matrix and initializing a model parameter set, performing numerical simulation by utilizing an oil reservoir numerical simulator to obtain prediction data, constructing a joint state matrix, performing dimensionless standardization processing on the joint state matrix to obtain an empirical correlation coefficient matrix, and then, performing optimization on the joint state matrix. And executing a graph lasso algorithm combined with an extended Bayesian information criterion to obtain an optimal dimensionless sparse precision matrix, extracting correlation coefficient sub-blocks required by Kalman updating from the optimal dimensionless sparse precision matrix based on a Scherr's theorem, obtaining a robust data auto-covariance matrix through a reverse reduction physical quantity outline, calculating Kalman gain in combination with an observation error covariance matrix, and calculating a Kalman filter. And the model parameter set is updated until the preset condition is met, the reservoir history fitting model parameter set is output, and the stability and precision of reservoir automatic history fitting are improved.

The invention provides a digital quantum simulation method, which comprises the following steps of: performing symmetry analysis and subspace decomposition on a target Hamiltonian to obtain an effective Hamiltonian and a symmetry constraint condition; constructing a target unitary operator according to the effective Hamiltonian, and initializing a quantum circuit population of the target unitary operator; calculating entanglement characteristics of each quantum line in the quantum line population, performing structure variation on the quantum line population based on the entanglement characteristics, and performing line fragment crossing in combination with a symmetry constraint condition to obtain a candidate line set; performing line parameter optimization on the candidate line set based on the fidelity gradient to obtain an optimized line set, and extracting gate distribution structure features of the optimized line set; and non-uniform sampling is carried out on the optimized line set based on gate distribution structure characteristics to obtain a sampling line subset, an optimal quantum line is screened out from the sampling line subset based on the deviation between an experimental observation value and a theoretical expected value, and quantum simulation is carried out based on the optimal quantum line.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The application relates to the field of flight control technology, in particular to a hovering control method and device for a short take-off and vertical landing aircraft of a tilt-rotor type, wherein the method comprises the following steps: constructing a hovering nonlinear dynamics model of the short take-off and vertical landing aircraft containing a tilt-rotor power system; converting the hovering nonlinear dynamics model of the short take-off and vertical landing aircraft into a high-order full-drive system model to design a state tracking controller; designing an adaptive law based on a neural network to estimate nonlinear terms in the hovering nonlinear dynamics model of the short take-off and vertical landing aircraft; and feeding back the nonlinear terms to the state tracking controller to perform hovering mode stable control. Thus, the problems that the hovering control method based on dynamic inversion needs a large amount of linearization processing, the actuator dynamic characteristics are significantly different, the controller needs to verify the closed-loop performance through repeated simulation and experiment, and the multi-actuator decoupling efficiency of the aircraft is low and the robust control performance is poor are solved.

The application belongs to the technical field of simulation analysis, and particularly relates to a switch cabinet condensation margin evaluation method based on simulation analysis and order reduction reconstruction, steps of which comprise: establishing a simulation analysis model of the switch cabinet, adding a physical field and performing physical field coupling and mesh partitioning; solving the simulation analysis model to obtain the temperature and humidity distribution inside the switch cabinet; optimizing the simulation analysis model; replacing electromagnetic heat with loss data as a heat source input into a non-isothermal flow field; constructing a physical field snapshot library for order reduction model training; processing the physical field snapshot library based on a POD method to construct an order reduction model; training a Kriging model to reconstruct the temperature and humidity field of the switch cabinet; constructing a condensation margin evaluation cloud map; in the actual operation process of the switch cabinet, calculating an actual dynamic dew point temperature to generate an actual operation condensation margin cloud map, and completing online real-time evaluation. The application can realize efficient and accurate reconstruction of the temperature and humidity field inside the switch cabinet and efficient evaluation of the condensation margin of the switch cabinet.

The invention is applicable to the technical field of unmanned aerial vehicle scheduling, and particularly relates to a regional route collaborative scheduling method and system for logistics unmanned aerial vehicles. Judging whether flight interference exists based on a preset minimum interference distance, acquiring flight information of the unmanned aerial vehicle participating in the flight interference, and sending the flight information to the corresponding unmanned aerial vehicle; yaw information calculation is carried out through the unmanned aerial vehicles according to the flight information and a built-in force field simulation obstacle avoidance model, and the unmanned aerial vehicles are controlled to fly based on the yaw information; and when the distance between the unmanned aerial vehicles is greater than the minimum interference distance, outputting return flight information based on the force field simulation obstacle avoidance model, and controlling the unmanned aerial vehicles to return to the flight route. According to the regional route co-scheduling method for the logistics unmanned aerial vehicles, each unmanned aerial vehicle is regarded as an agent which autonomously moves in a three-dimensional force field, complex global path planning and real-time obstacle avoidance are decoupled, and the dimensionality and complexity of distributed calculation are greatly reduced.

The application is suitable for the field of artificial intelligence technology, and provides a word vector representation method and device based on language processing and a terminal device, the method comprising: constructing an input word table; setting word frequency information of each word in the input word table; classifying the words into N clusters according to the word frequency information, words with the same word frequency information being classified into the same set to obtain a clustering set with N subsets; defining a first word vector dimension of the words in the first cluster through an experience factor, and obtaining a second word vector dimension of the words in each subset according to the subset where the words are located and the first word vector dimension; defining an nth linear mapping matrix for the nth cluster; and processing a target word vector through parameters shared by the input word table as an input of a natural language processing model. Through the application, the problem that the calculation amount is large, the efficiency is low and the word distribution features cannot be considered when converting words into word vectors in natural language processing can be solved.

This invention provides a method for identifying the authenticity of wheat flour based on the fusion of Raman and near-infrared spectroscopy, belonging to the field of wheat flour authenticity identification technology. The method includes: acquiring Raman and near-infrared spectral data of the wheat flour sample to be tested, and preprocessing them separately; constructing and training a fusion detection model, which includes a data-level fusion module, a feature-level fusion module, and a decision-level fusion module; the data-level fusion module generates full-spectrum fusion data; the feature-level fusion module concatenates core features into a comprehensive feature set; the decision-level fusion module inputs the comprehensive feature set into a hybrid model; and the trained fusion detection model processes the comprehensive feature set to output the authenticity identification result of the wheat flour sample to be tested. This invention, through a three-level spectral fusion architecture and a dynamic adaptive adversarial mechanism, can effectively achieve high-precision and high-robust identification of wheat flour authenticity.

The application discloses a fast robust monocular vision inertial positioning method and system, comprising the following steps: configuring IMU and monocular camera sensor parameters; IMU data preprocessing; IMU attitude initialization; extracting image features of a monocular camera, removing abnormal matching points and performing IMU pre-integration; monocular vision initialization; odometer attitude optimization, comprising the following steps: judging whether a feature point is mis-matched based on an IMU pre-integration result, constructing a feature point re-projection error Jacobian matrix, and optimizing inverse depth of the feature point and image frame attitude through block and diagonalization processing of the re-projection error Jacobian matrix; after completing current frame attitude updating, selecting a key frame based on a key frame identification rule; and judging according to a current frame, removing a certain frame in a sliding window to reserve a sliding window space for adding a latest frame. The application optimizes inverse depth of a feature point and image frame attitude through block and diagonalization processing of a re-projection error Jacobian matrix, and reduces computational complexity.

The application provides a lightweight identity recognition method combining voiceprint and earprint features. The method comprises the following steps: obtaining voice and ear canal echo signals of a known registered person and a person to be verified; extracting and fusing 13-dimensional mel-frequency cepstral coefficients (MFCC) of the voice and ear canal echo signals, i.e., obtaining voiceprint and earprint fusion features; inputting the fusion features into a lightweight identity recognition model to extract 128-dimensional embedding features of the known registered person and the person to be verified; calculating the similarity of the embedding features of the two types of persons by using a probabilistic linear discriminant analysis (PLDA) method; and determining whether the person to be verified is the known registered person according to the similarity. In the application, the fusion of earprint and voiceprint features improves the recognition performance of the classification model. The lightweight identity recognition model is obtained through a pre-training process, which reduces the equal error rate (EER) and greatly reduces the parameter quantity of the identity recognition model.

The invention discloses an electric power resource interactive processing scene model training method and device, an electric power resource interactive processing method and device, equipment and a medium. The method comprises the following steps: applying unit systems in the electric power resource interaction system, establishing a training model for each unit system in the electric power resource interaction system, constructing a model structure in which a strategy network and a value network are coordinated, and performing value difference calculation and parameter adjustment by combining state data, interaction operation and value feedback of the unit systems at different moments. Therefore, the interactive operation decision of the unit system in the power resource interactive system is based on the own state data. According to the embodiment of the invention, the model training efficiency and the decision-making efficiency of power resource interaction operation can be improved.

The invention discloses a carbon emission calculation method and system based on building main material equivalent, belongs to the technical field of carbon emission calculation, and solves the problems that an existing carbon emission calculation method is high in data requirement, low in calculation speed and insufficient in applicability. By analyzing historical project data, screening out key principal materials and constructing an algorithm model between consumption and total carbon emission, 'less data and high precision 'rapid calculation is realized, indirect carbon emission generated by building material production, transportation and construction is mapped to the key principal materials through weights, a whole-process carbon emission equivalent evaluation system is established, and the evaluation accuracy is improved. And the whole-process carbon emission can be output in a one-stop manner by inputting the consumption. The model output is an interpretable regression equation with a clear coefficient weight, a transparent decision basis is provided, and in addition, the key main materials are pre-determined through an algorithm, so that a new project can directly utilize the use amount of the existing key main materials in the project cost process to carry out carbon emission quick calculation.

The application provides a kind of robust laser SLAM repositioning and mapping method for embodied intelligent robot in the technical field of robot autonomous navigation, comprising: receiving original laser radar point cloud and IMU data, pre-processing point cloud to extract feature points;IMU pre-integration provides initial pose constraint;Loop closure detection is through the two-stage cascade of ScanContext global search and LinK3D local matching, obtains repositioning constraint;Front-end odometry matches feature points with local voxel map, combines IMU optimization to output pose estimation, filters key frame to generate laser odometry factor;With repositioning constraint as initial value, use improved ICP to accurately register to obtain repositioning factor;Finally, construct factor graph, jointly optimize IMU, laser odometry and repositioning factor, solve global consistent pose sequence and map.The application has the advantages that: it greatly improves the system robustness, repositioning accuracy and global map consistency of quadruped robot in severe motion and large-scale scene, while meeting the real-time constraint of embedded platform.

The application discloses an INS / GNSS rod arm error online estimation method based on dimension reduction filtering. The method splits the INS / GNSS rod arm error and other error variables, constructs multiple low-dimensional filters, reduces the coupling between the error variables, has clear mechanism, simplifies the model, reduces the requirements of the combined navigation algorithm on the chip main frequency and the storage space, improves the calculation speed and the filtering estimation precision, and can improve the positioning performance of the vehicle-mounted combined navigation system after the INS / GNSS rod arm error online estimation and compensation, and has high engineering application value.

This invention provides a hybrid-mode MRE vibration isolator and its design method. The hybrid-mode MRE vibration isolator is installed on the drive shaft of rotating equipment and includes an irregularly shaped stacked MRE. The irregularly shaped stacked MRE is frustum-shaped, and contains multiple layers of spacers and MRE material layers arranged in parallel at intervals. The spacers are made of ferromagnetic metal. By employing a frustum-shaped stacked MRE with different upper and lower diameters, this hybrid-mode MRE vibration isolator reduces the dimension of the controlled object compared to existing cylindrical MRE vibration isolators. This hybrid-mode MRE vibration isolator directly transfers abnormal vibration energy from large rotating equipment to the MRE material through the drive shaft. The change in MRE stiffness can suppress abnormal vibrations. This vibration isolator is suitable for intelligent vibration reduction of large rotating equipment, especially in rotating shear applications.

A computer-implemented method for determining one or more application-specific control parameters of a pump operating in a system for moving a fluid, the method comprising: monitoring one or more operational variables of the pump during operation of the pump in said system; applying a trained machine-learning model to automatically determine an application type from the monitored operational variables, the application type representing a type of system the pump operates in and / or representing a type of operation performed by the pump when operating in said system, each application type being associated with a respective set of one or more application-specific control parameters; controlling the pump based on the set of one or more application-specific control parameters associated with the determined application type.

The present application relates to the technical field of material design, and particularly relates to a prediction model construction method for T 50 and T 90 in a platinum-based bimetallic catalytic material propane oxidation reaction, the present application performs weighted operation on intrinsic physicochemical parameters of an active metal content ratio as a weight to construct weighted features, removes redundant features to form a feature pool through a Pearson correlation coefficient, performs systematic evaluation on seven kinds of mechanism different regression models under a leave-one-out cross-validation framework, and selects a gradient boosting regression tree as an optimal model, performs exhaustive search screening on remaining variable features after fixing calcination temperature, calcination time and reaction atmosphere as key process features, determines an optimal feature combination composed of fixed features, active metal melting point, active metal conductivity, carrier formation energy and carrier band gap, and establishes a prediction model according to the optimal feature combination. The model of the present application is subjected to gridding virtual screening and closed-loop verification of experiments, the prediction value is highly consistent with the experimental value, and the model can replace a large number of preliminary screening experiments.

The application provides a robot and a control method, device and readable storage medium thereof. The control method comprises: acquiring a point cloud image containing a grasped object; inputting the point cloud image into a preset grasping network to enable the preset grasping network to perform classification prediction according to the point cloud image to obtain a contact point between a fingertip and the grasped object; constructing a first rotation matrix of a coordinate system with the contact point as an origin relative to a world coordinate system according to coordinate information of the contact point; determining coordinate information of a joint according to the coordinate information of the contact point, a vector between the joint and the fingertip and the first rotation matrix; determining a transfer matrix of a coordinate system with the joint as an origin relative to the world coordinate system according to the coordinate information of the joint; and controlling the robot to work in a grasping posture corresponding to the transfer matrix.

The invention relates to a method and apparatus that can search for one or more optimal matching coating formulas in a database based on measured data of a target coating, i.e. search for one or more preliminary matching formulas within the database; and can refine the search using deep learning techniques using an image similarity measure between an image of the one or more optimal matching coating formulas on one side and an image of the target coating on the other side.

The application discloses a five-dimensional seismic data interpolation method and system based on coordinates, constructs a point-by-point seismic data interpolation model based on a NeRF theory, modifies an MLP framework by combining an additional convolution layer on the basis of the point-by-point interpolation model, constructs a profile-by-profile seismic data interpolation model, introduces kernel norm regularization to construct an objective function, reads original five-dimensional seismic data D obs and inputs the five-dimensional seismic data with missing traces into the network to realize reconstruction of the five-dimensional seismic data. The application eliminates the need for additional labeled data, utilizes unique characteristics of seismic data, enables a convolution network decoder to output data profile by profile, can significantly improve data processing efficiency by 40 times, and increases kernel norm regularization in the objective function, so that the noise resistance and robustness of the model can be effectively improved.

The application discloses a network security management and control method and system based on different scenes, and the method comprises the following steps: when the number of elements in a to-be-responded queue is less than a preset number, performing SQL injection detection on each request parameter based on a first interception rule to obtain an SQL injection detection result; when the number of elements in the to-be-responded queue reaches the preset number, simultaneously performing SQL injection detection on each request parameter based on a second interception rule to obtain an SQL injection detection result; and based on the SQL injection detection result obtained based on the first interception rule or the second interception rule and the request parameter to be responded, performing a corresponding response operation. The application can provide corresponding SQL injection detection schemes for different scenes, and can accelerate the response speed of a large number of SQL access requests in a short time.

The present application relates to the field of information technology and intelligent warehousing technology, in particular to a digital twin and multi-objective optimization fusion enterprise intelligent warehousing scheduling method and system, the method comprising: receiving a trigger signal, collecting first monitoring data; making a target decision to obtain a plurality of pending targets; determining a monitoring group, matching the corresponding signal conditioning strategy; collecting second monitoring data, preprocessing the second monitoring data, and conditioning the preprocessed second monitoring data; making a secondary decision on the plurality of pending targets to obtain at least one execution target, and issuing the execution target and executing it. The present application effectively solves the problems of creating a high-precision digital twin model and maintaining real-time synchronization of massive data consuming huge computing resources by constructing a two-stage hierarchical decision-making framework from coarse to fine. Applied to warehouse scheduling, a high-confidence scheduling strategy can be obtained without completely replicating the reality details.

The invention discloses a humanoid robot real-time limb trajectory optimization method and device based on centroid dynamics and a robot, and belongs to the field of robot motion control. The method comprises the following steps: acquiring a robot take-off state and a next grounding target; based on the conservation of angular momentum in the flying stage, constructing an optimization problem which takes a joint trajectory polynomial coefficient as an optimization variable and takes the minimization of the body direction deviation during grounding as a target and comprises kinematics position and speed constraint; the core of the method is that the key property that a translation coupling term in a centroid angular momentum matrix is zero is utilized and proved, so that body rotation dynamics and centroid translation are completely decoupled, and a complex whole-body trajectory is optimized and simplified into an efficient problem which can be solved in real time at a millisecond level; according to the method, a coordinated limb swinging track can be automatically generated, the body posture is accurately controlled in the running and jumping flying stage, and manual intervention is not needed.

The invention discloses a protocol identification method for an electric power Internet of Things terminal, which comprises the following steps: interacting with the electric power Internet of Things terminal, acquiring protocol data in the interaction process, and carrying out data preprocessing on the protocol data to form an effective load data set; performing feature extraction on the effective load data in the effective load data set to obtain input features; inputting the input features into a power Internet of Things terminal equipment protocol classification model constructed based on a density space clustering method, carrying out learning training, and outputting a classification model after training is finished; and inputting the to-be-classified present network protocol message data subjected to data preprocessing and feature extraction into the trained classification model for classification, and outputting a classification result. The method does not depend on manual labeling any more, the influence of manual intervention and errors is reduced, the method can quickly adapt to access of new equipment and change of protocols in the dynamic environment of the power Internet of Things terminal equipment, and the stability and safety of a power system are guaranteed.

A method is provided for studying the behaviour of an animal in an experimental area using a camera to obtain a video stream having a plurality of images of the area, removing background noise from the images, extracting parameters from the images to form multidimensional data points, clustering data points so that each cluster represents and animal behaviour and outputting a matrix of results. Apparatus for performing the method is also provided.

This invention relates to the field of drone scheduling technology, and particularly to a method and system for regional route collaborative scheduling of logistics drones. The method includes: constructing a drone monitoring platform; determining whether flight interference exists based on a preset minimum interference distance, acquiring flight information of drones involved in the interference, and sending it to the corresponding drones; calculating yaw information for each drone based on the flight information and a built-in force field simulation obstacle avoidance model, and controlling the drones to fly based on the yaw information; when the distance between drones is greater than the minimum interference distance, outputting return-to-home information based on the force field simulation obstacle avoidance model, and controlling the drones to return to their flight paths. This invention provides a regional route collaborative scheduling method for logistics drones that treats each drone as an intelligent agent autonomously moving in a three-dimensional force field, decoupling complex global path planning from real-time obstacle avoidance, and greatly reducing the dimensionality and complexity of distributed computing.

The application discloses a method for quantitatively analyzing mineral content and element content of sedimentary rock cores, and belongs to the field of quantitatively analyzing mineral content and element content. The method comprises the following steps: collecting a plurality of sedimentary rock core samples to obtain a mineral abundance matrix and an element content matrix; performing grey correlation analysis on minerals and elements based on the mineral abundance matrix and the element content matrix to obtain a grey correlation coefficient matrix; calculating the maximum information coefficient of minerals and elements based on the mineral abundance matrix and the element content matrix to obtain an information coefficient matrix; performing weighted summation on the grey correlation coefficient matrix and the information coefficient matrix to obtain a joint score matrix, screening out mineral-element combinations with scores greater than a threshold value, and obtaining a candidate mineral-element combination set; and performing HSI high-spectrum mineral identification-XRF element analysis bidirectional guided modeling. The application solves the problems that element-to-mineral mapping is not unique and elements and minerals are not bidirectionally constrained in the prior art.

The invention discloses a micro-grid large signal stability analysis method for controlling a heterogeneous inverter, and relates to the technical field of stability analysis. Comprising the following steps: constructing a plurality of GFM inverter equivalent oscillator models and a plurality of GFL inverter equivalent oscillator models of the series-parallel island microgrid; all the equivalent vibrator models are simultaneously established, and an island series-parallel micro-grid network vibrator model is obtained; respectively determining a first stability domain between each GFM inverter and a paired GFM inverter connected with the GFM inverter, and a second stability domain between each GFM inverter and a paired GFL inverter connected with the GFM inverter; and traversing and crossing the first stability domain and the second stability domain of each GFM inverter to obtain the overall frequency stability domain of the system, and determining the large signal stability according to the overall frequency stability domain of the system. According to the method, the dimensionality and complexity of the stability criterion are reduced, and the criterion of the large signal stability of the micro-grid is easier to analyze and calculate.

The invention provides a multi-source emission and quality collaborative optimization method for a mixing and injection molding integrated production line. The method is particularly suitable for a mixing and injection molding integrated production line, and aims to synergistically optimize the multi-source environmental emission and the product quality of the mixing and injection molding integrated production line. Through establishment of a quantitative model and multi-target optimization solution, process parameter optimization of emission reduction and quality guarantee targets is realized. By adjusting key process parameters of a production line, reduction of carbon emission, VOCs emission and PM2.5 emission is completed on the premise that the product percent of pass or quality requirements are guaranteed. The invention provides a decision support method which is close to the actual production condition of a production line for reducing the comprehensive environmental emission on the premise of ensuring the product quality of the mixing injection molding production line through systematic modeling and an optimization framework.

This invention relates to a speech denoising method and device based on deep compressed sensing, comprising the following steps: adding Gaussian white noise to a clean speech signal to obtain a noisy speech signal; performing time normalization and frame segmentation on both the clean and noisy speech signals; extracting perceptual features from both the clean and noisy speech signals to obtain perceptual features of the clean speech and the noisy speech; training the GAN using the perceptual features of the noisy speech as input to a generative adversarial network (GAN) and the perceptual features of the clean speech as the objective function to obtain perceptual features for generating clean speech; and reconstructing the generated clean speech signal using the OMP reconstruction algorithm based on compressed sensing. This invention, through deep compressed sensing, is suitable for nonlinear noise analysis and processing, improving the denoising effect of noisy speech signals.