24results about How to "Short calculation time" patented technology

The invention relates to an MIMO system modulation type identification method and system under communication interference, and belongs to the technical field of signal identification, and the method comprises the steps: carrying out the filtering, denoising and whitening preprocessing of a received MIMO signal; a five-dimensional fourth-order mutual cumulant tensor is constructed by using the preprocessed signal, and a separation matrix is estimated through parallel factorization, so that suppressing interference reconstruction separation is realized; constructing a three-dimensional tensor again for the separated and suppressed signal, and performing dimensionality reduction and feature extraction on the tensor by using a generalized tensor discriminant analysis algorithm; and performing Tucker decomposition on the tensor after dimension reduction to establish a kernel tensor and a factor matrix, and identifying the similarity of the kernel tensors of different modulation types according to the Euclidean distance criterion to complete MIMO signal modulation type identification. Simulation results show that the method has good recognition accuracy under the condition of low signal to interference ratio, and prior information such as channel coefficients and noise distribution is not needed.

The invention relates to the field of cloud computing and deep reinforcement learning, and discloses a large-scale scientific computing workflow scheduling method based on deep reinforcement learning. According to the method, the graph attention network is introduced to extract workflow structure features, and a deep reinforcement learning sampling strategy based on a genetic algorithm is designed to generate an initial sample trajectory for a deep reinforcement learning agent, so that the training process is accelerated. Experiments show that a faster training speed can be obtained in a simulated cloud environment, the workflow is executed on the cloud computing platform according to the generated large-scale scientific computing workflow scheduling strategy, and better completion time, energy consumption and reliability can be reflected.

The application is a kind of automation testing and application fluency testing method based on control matching, relating to the field of Android end automation testing.The application method comprises: setting a test script containing behavior simulation and fluency determination in advance, reading the corresponding test script after the Android end receives a test command; acquiring interface control structure positioning target control, executing simulation operation; starting timing after each simulation operation, and acquiring all control elements of the current interface when reaching the set time threshold, judging whether the interface is loaded or not in combination with the interface structure information recorded in the fluency determination script; obtaining application fluency determination result according to the interface loading condition at different time thresholds and returning to the server end.The application does not need to acquire ROOT permission, does not need to connect PC end for testing, is safer, is more convenient to use, reduces the adaptation cost, and is higher in testing accuracy and testing speed.

The application discloses a creep life prediction method for a nickel-based single crystal alloy considering a primary orientation deviation angle, and comprises the following steps: S1, acquiring a primary orientation deviation angle of a sample; S2, performing a creep test on the sample to obtain creep test data; S3, establishing a creep constitutive damage equation, constructing a correlation formula of a primary orientation deviation angle influence factor and the primary orientation deviation angle, and introducing the primary orientation deviation angle influence factor into a material degradation term in the creep constitutive damage equation for correction; S4, calibrating undetermined parameters in the corrected creep constitutive damage equation by using the creep test data; and S5, establishing a finite element model of a nickel-based single crystal high-temperature alloy sample based on the calibrated creep constitutive damage equation, and calculating the creep life of the sample by using the finite element method. The application changes the influence of the primary orientation deviation on the creep life from "systematic theory coupling" to "engineering precise quantification", and is more suitable for actual engineering application requirements.

The invention provides a method and system for calculating the opening number of eyelets, computer equipment and a medium. The method comprises the steps that a seam pressure calculation equation is obtained through coupling; carrying out two adjacent discharge capacities and two seam pressures, and carrying out coupling to obtain a calculation equation of the opening number of the eyelets; according to the obtained hole opening number calculation equation, the number N1 of opened holes in the pump stopping stage before the temporary plugging material is added, the number N2 of remaining opened holes in the pump starting stage after the temporary plugging material is added and the number N3 of newly opened holes in the pump stopping stage after the temporary plugging material is added are calculated in the fracturing construction process of each section; according to the hole number N1 and the hole number N2, the hole number N4 plugged by the temporary plugging material is obtained; and according to the hole number N3 and the hole number N4, the total hole opening number N5 after construction is completed is obtained. The system comprises a data collection module, a data processing module and a digital display module which are connected in sequence. The method has the beneficial effect that the method can be popularized and applied on a large scale.

The present invention discloses a method for eliminating the influence of BLE direction finding carrier frequency deviation to obtain phase delay, so that the phase delay only related to the direction of the incident signal source can be obtained with extremely low computing resources without estimating the carrier frequency deviation, thereby improving the accuracy of the BLE direction finding system in the presence of carrier frequency deviation and improving the stability of the BLE direction finding system under various operating temperature conditions.

The invention discloses a vehicle cluster cooperative positioning method based on an MDS-IDBO algorithm. The method comprises the following steps: step 1, constructing a multi-node cooperative positioning model; step 2, calculating relative coordinates by using an MDS-MAP algorithm; step 3, anchor node positioning correction is carried out by using an IDBO algorithm; 4, recalculating the distance square matrix and the relative coordinates of the nodes according to the step 1 and the step 2 by using the anchor nodes positioned and corrected in the step 3; 5, the distance square matrix and the node relative coordinates are utilized, a positioning error function serves as a fitness function to be substituted into an IDBO algorithm, an optimal coordinate transformation matrix is obtained, and absolute coordinates are calculated. The method is suitable for the technical field of communication, node positioning correction is carried out based on the MDS-MAP algorithm, errors caused by BDS / GPS positioning can be corrected to a great extent, and the accuracy of node positioning correction is improved. The provided IDBO algorithm is used for optimizing the MDS-MAP algorithm, the algorithm complexity is low, the calculation time is short, and the positioning precision is high.

The invention provides a capacitance current calculation method under distributed compensation. The method comprises the following steps: S1, fitting a zero-sequence transient voltage of a bus into a sine function of which a rectangular coordinate is a time function; s2, setting a line terminal zero sequence voltage initial value; s3, calculating bus zero-sequence voltage and line outgoing line zero-sequence current according to the line distribution parameter circuit model; s4, comparing the calculated value of the bus voltage with an actual value, and calculating an error; s5, judging whether the error meets the precision requirement or not, and if yes, outputting line outgoing line zero-sequence current; and if not, correcting the initial value of the zero-sequence voltage of the line terminal and repeating the steps S3-S5. According to the method, the calculation error can meet the requirement of capacitance current compensation, the calculation time of the capacitance current is short, and the requirement of online adjustment of the arc suppression coil can be met.

The application discloses a kind of self-adapting polishing system and method of single type deflection reach stacker girder, comprising: line sweep laser sensor, flexible tooling, controller, polishing robot end;Polishing method is: collecting girder laser scanning data and analysis, obtain girder pose data and adjust girder to standard pose according to girder pose data;Scan girder surface under standard pose, respectively obtain the deflection deformation of girder surface, the appearance of weld surface;And according to the appearance of weld surface, obtain weld width, height data and automatically adjust polishing processing parameters;Polishing trajectory is generated based on adjusted polishing processing parameters for controlling polishing robot end;The application is by integrating workpiece pose adjustment algorithm, polishing trajectory guiding algorithm and laser line sweep sensing technology, to solve adaptive polishing problem, improve artificial polishing effect, to realize the automation of adaptive polishing process with deflection deformation girder guiding and improve the environmental problem caused by polishing.

This application provides a method and apparatus for cutting quantum circuits. The method includes dividing the quantum circuit to obtain two bits of data; generating cutting position data based on the quantum circuit and the two bits; cutting the quantum circuit based on the cutting position data to obtain circuit cutting information, which includes two initial quantum sub-circuits; updating the wire cutting position and gate cutting position to obtain new cutting position data if either initial quantum sub-circuit does not meet the bit number constraint; determining the circuit cutting cost for generating new circuit cutting information based on the new cutting position data if the new initial quantum sub-circuit meets the bit number constraint; and determining the target circuit cutting information from multiple new circuit cutting information or from existing circuit cutting information based on the circuit cutting cost.

This invention belongs to the technical field of robotic arm motion planning and discloses a robotic arm motion planning method and system based on an error-driven adaptive gradient model. The method includes: S1 establishing the joint velocity at two adjacent moments with respect to the adaptive coefficient λ based on a Lyapunov function. n The gradient relationship is obtained; S2. The joint velocity at time n+1 is calculated using the gradient relationship, and the error between the calculated joint velocity at time n+1 and the adaptive coefficient is updated using the central finite difference method; S3. n = n+1; return to step S2 until the joint velocities at all times are obtained; S4. The joint position is calculated using the relationship between the joint velocity and position; the velocity of the end effector is calculated using the robotic arm motion planning model and the joint velocities at each time, thereby realizing the motion planning of the robotic arm. This invention solves the problem of low computational efficiency in robotic arm motion planning.

This application belongs to the field of radar reconnaissance technology and provides a single-source radiation determination method based on temporal information entropy. It first calculates the Time Difference of Arrival (TDOA) of all adjacent pulses, then sets a clustering threshold and a first-class cluster center, and uses a clustering algorithm to obtain multiple clusters based on the TDOA. Next, it reduces the number of clusters and removes invalid elements by merging and removing harmonics. The distribution probability of each TDOA is calculated based on the number of indices in the merged clusters after harmonic removal. The temporal information entropy is calculated based on the distribution probability of each TDOA. A set information entropy discrimination threshold is set; if the temporal information entropy does not exceed the threshold, the pre-sorted signal is considered to be composed of a single radiation source; otherwise, it is composed of multiple radiation sources. This method can accurately determine the composition of radiation source signals in the radar pre-sorting clustering results, avoiding further PRI main sorting processing of single-source signals and reducing redundant sorting calculations.

A robust demosaic bilinear interpolation method for polarization hyperspectral imaging is presented, addressing issues such as optical system platform vibration, frame loss in acquired images, and spectral aliasing in existing technologies. The method includes acquiring a set of polarization hyperspectral images and performing robust demosaic reconstruction on them. Image I is the acquired image of the Kth spectral band, and mosaic image II is an empty matrix used as the interpolation space for image I. Horizontal values ​​are assigned to mosaic image II, and boundary conditions are applied for filling to complete the horizontal interpolation. The intensity images of the four polarization channels in different directions in mosaic image II are extracted, and vertical interpolation is performed on these polarization channel intensity images. This completes the polarization demosaic reconstruction of the Kth spectral band image. This method is applicable to suppressing image artifacts generated during reconstruction.

The invention discloses a method for optimizing and adjusting the demagnetization current of a distributed demagnetization system. The method comprises the following steps: searching for the flight speed and position of an individual, and calculating the fitness function value of each individual at the current position; for each individual, comparing the fitness function value of the current position with the fitness function value of the current individual historical optimal position, and determining the current individual historical optimal position; for all individuals, comparing the fitness function value of the current individual historical optimal position of each individual with the fitness function value of the current global historical optimal position, and determining the current global historical optimal position; judging whether a termination condition is met according to the current global historical optimal position, and terminating the adjustment of the demagnetization current if the demagnetization current corresponding to the current global historical optimal position reaches a preset value; the invention optimizes the calculation algorithm, simplifies the calculation procedure, and shortens the calculation time, so as to minimize the peak error of the residual magnetic field after the ship is demagnetized.

The present invention proposes a vibration signal recognition method and system based on improved multi-scale permutation entropy. The method includes: collecting vibration signals under different working states, performing multi-scale analysis based on sliding windows and sample quantiles on the collected vibration signals under different working states, and obtaining a sequence after multi-scale analysis; obtaining the permutation entropy of the sequence after multi-scale analysis, obtaining a multi-scale permutation entropy value, and using the value of the multi-scale permutation entropy as a feature vector of the vibration signal under different fault states; inputting the feature vector into a classifier based on machine learning, thereby realizing the recognition of vibration signals under different fault states and obtaining a fault state recognition result; and verifying the rationality of the recognition method according to the fault state recognition result. The present invention solves the defect that the conventional multi-scale permutation entropy fault diagnosis algorithm has insufficient ability in fault feature extraction, and has a short calculation time, thereby improving the fault diagnosis efficiency.

The invention discloses a satellite attitude super-agile sweep neural network minimum parameter learning sliding mode control method, which belongs to the technical field of microsatellite attitude control, and comprises the following steps: S1, in a satellite control process, establishing an actual output model of a neural network; s2, introducing a minimum parameter learning method to replace weight adjustment of a radial basis function neural network, and converting calculation of a neural network weight matrix W into solution of a single parameter # imgabs0 #; according to the method, the system state is converged to zero within finite time by using fast Terminal sliding mode control, and a switching item in a sliding mode is approached through a radial basis function neural network, so that the aim of weakening buffeting is fulfilled; meanwhile, a minimum parameter learning method is designed to replace weight adjustment of the radial basis function neural network, a large amount of operation of a weight matrix is avoided, the efficiency of the controller is effectively improved, and the method has the advantages of being good in convergence, imaging and control effect.

The invention discloses a shallow sleeve type buried pipe soil heat conductivity distribution measuring method, and belongs to the technical field of ground source heat pumps. The method comprises the following steps: carrying out a distributed thermal response test on the shallow sleeve type buried pipe, and layering the shallow sleeve type buried pipe in the vertical direction; sequentially carrying out linear fitting on the measured value of the average temperature of the annular fluid of each layer and the natural logarithm of the time to obtain the slope of a linear relation; constructing a one-dimensional heat exchange model for each layer in sequence, wherein the one-dimensional heat exchange models are respectively used for simulating the average temperature of annular fluid of each layer and calculating the slope of a linear fitting relational expression of natural logarithms about time; for any layer, taking the absolute deviation between the slope based on the actual measurement value and the slope based on the simulation data of the one-dimensional heat exchange model of the layer as an objective function of the layer, and further calculating the soil heat conductivity corresponding to the minimum objective function, namely the measured soil heat conductivity of the layer; through the steps, the thermal conductivity of each layer of soil is measured in sequence. The method is high in precision, simple and suitable for engineering application.

The embodiment of the invention discloses a gradient information storage method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring first capacity information corresponding to a target video memory space for storing gradient information of a neural network model; obtaining video memory size information corresponding to each group of gradient information of the neural network model, wherein each group of gradient information corresponds to a unique computing node of the neural network model; and according to the first capacity information and video memory size information corresponding to each group of gradient information, storing to-be-reserved gradient information in the neural network model in a target video memory space, and deleting gradient information except the to-be-reserved gradient information, the video memory size occupied by the to-be-reserved gradient information is smaller than or equal to the storage capacity of the target video memory space. According to the scheme, gradient information generated in the neural network model training process can be adaptively reserved according to the storage capacity of the video memory space.

The invention relates to an energy storage locating and sizing method considering power distribution network fault recovery under extreme disasters, and the method is technically characterized in that a plurality of non-dominated solutions close to Pareto optimum are obtained through the global search optimization of a multi-target particle swarm algorithm based on consensus; a shortest path method is used for searching a power grid fault recovery area under an extreme disaster, a site selection scheme capable of meeting a power supply recovery target is screened out from the power grid fault recovery area, and a multi-target energy storage site selection and sizing problem considering economic benefits and fault recovery is converted into a single-target energy storage site selection and sizing optimization problem. And solving the power distribution network energy storage device addressing and sizing optimization model by using an interior point method to obtain a power distribution network energy storage device addressing and sizing scheme meeting the requirements of an economic index and a fault recovery index. The method is reasonable in design, the optimal solution meeting user preferences can be quickly obtained, and the load recovery requirement when the power grid breaks down is met.

The invention relates to the technical field of wind power plant frequency modulation power control, and provides a power distribution method and device for wind power plant overspeed variable pitch collaborative load shedding and electronic equipment. The power distribution method for wind power plant overspeed variable-pitch cooperative load shedding comprises the steps of obtaining structure parameters, wind speed information, pitch angle information and variable-pitch frequency modulation response time limit values of fans in a wind power plant and operation load shedding rate requirements of the wind power plant; based on the acquired data, constructing a linear programming problem by taking the variable quantity of the pitch angle of the fan in the wind power plant as an optimization variable; according to an optimization result of the linear programming problem, determining a pitch angle variable quantity of a fan in the wind power plant; and outputting a power reference value of the fan in the wind power plant based on the determined pitch angle variable quantity. According to the method, finer power distribution is achieved, meanwhile, the calculation time needed by the linear programming problem is short, and online application of the method in a wind power plant is facilitated.

The invention relates to a fault diagnosis method and device based on a simulated annealing dynamic polynomial network, computer equipment and a storage medium. The method comprises the following steps: acquiring a preset number of state data sets of an object to be subjected to fault diagnosis; constructing a to-be-trained network structure which sequentially comprises an input layer, a monomial and order selection layer, a polynomial layer, a full connection layer, a Softmax layer and an output layer in the direction from input to output; a specified loss function is adopted to obtain a loss value of the to-be-trained network structure, iterative training is carried out on the to-be-trained network structure according to the loss value in combination with a BP algorithm, an Adam algorithm and a simulated annealing algorithm, and a simulated annealing dynamic polynomial network with the state data set as input and the fault diagnosis data set as output is obtained; and inputting the state data set into the simulated annealing dynamic polynomial network to obtain a fault diagnosis data set of the object to be subjected to fault diagnosis. By adopting the method, the diagnosis or prediction result of the object to be subjected to fault diagnosis can be obtained, the calculation time is short, and the application range is wide.

An early fault diagnosis method for cross-stage multi-condition based on permutation spectrum difference measurement, including: collecting vibration signals of mechanical equipment under different states in the manufacturing test stage and the actual use stage, dividing the vibration signals into multiple subsequences through a combination of characteristic parameters, and reconstructing the subsequences into the original phase space; obtaining all possible permutation patterns according to the embedding dimension of the vibration signals, and converting the original phase space into a symbolic phase space; counting the number of occurrences of various permutation patterns in the symbolic phase space to obtain permutation spectrum characteristics; obtaining the difference degree between the vibration signals collected under different states; establishing a parameter selection model to select the best parameter combination; calculating the permutation spectrum characteristics of each time series using the best parameter combination, inputting the permutation spectrum characteristics of the data in the manufacturing test stage into a multi-class SVM classifier to obtain a training model, and inputting the permutation spectrum of the data in the actual use stage into the training model to identify the fault type and operating condition corresponding to the time series.

The application discloses an image recognition method based on quaternion generalized kernel sparse principal component analysis, and the method comprises the following steps: acquiring training and test sample images; extracting the entropy, red, green and blue four component information of each image, and performing quaternion matrix representation on the information to construct a corresponding quaternion real representation matrix; constructing a corresponding quaternion covariance kernel matrix and a quaternion p-norm Euclidean distance according to the quaternion real representation matrix, and then constructing a quaternion generalized kernel sparse principal component analysis optimization model; solving the optimization model, taking the calculated kernel sparse principal components of the training sample in the row and column directions as the final solution; calculating the projection matrix of the training and test sample covariance kernel matrix according to the final solution of the model in the row and column directions; and using the quaternion p-norm Euclidean distance to recognize the category to which the images in the test sample set belong, so that the recognition accuracy and robustness are improved.

This invention provides a crowd density analysis method based on dual-path collaboration, comprising: constructing an analysis model, the analysis model including a first generative model, the first generative model taking an image as input and outputting a crowd density map of the image; training the analysis model, comprising: constructing a training set; constructing a first generative adversarial network (GAN) using the first generative model and a first discriminant model, and inputting the training images from the training set into the first GAN; constructing a second GAN using a second generative model and a second discriminant model, randomly segmenting the training image into multiple sub-training images, and inputting these sub-images into the second GAN; optimizing the first generative model using the loss functions of the first and second GANs; and inputting the original image into the trained analysis model to obtain the crowd density map of the original image. This invention also provides a system. This invention uses only the first generative model when analyzing the original image, reducing model overhead and complexity.