11results about How to "Short calculation time" patented technology

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 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.

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.