44results about How to "Solve computational difficulties" patented technology

The invention relates to a full-automatic splicing type box machine and a method for processing packing boxes. The full-automatic splicing type box machine comprises a bottom plate conveying and glue spraying station and a product forming station. The product forming station is connected with a left side plate conveying and erecting station and a right side plate conveying and erecting station, and a left plate erecting mechanism and a right plate erecting mechanism are respectively arranged at the two side plate conveying and erecting stations. The method for processing the packing boxes includes a technological procedure for conveying plates of the packing boxes, automatically spraying glue and forming and reinforcing the packing boxes by the aid of molds. The full-automatic splicing type box machine and the method have the advantages that a technological process is simple and feasible, effects of saving materials and reducing the cost are realized, the products are firm and attractive after being formed owing to an automatic glue spraying technology, a packing box processing procedure is automatic, and the full-automatic splicing type box machine and the method are favorable for organizing production and greatly improving the labor productivity.

The invention relates to a rational-polynomial-coefficients (RPC)-based method for improving and calibrating the block adjustment of a three-linear array three-dimensional satellite, which comprises the following steps of: (1) determining the mapping relationship between ground points and image points, namely a strict imaging model; (2) solving RPC parameters of each image in three linear arrays according to the strict imaging model, and searching for connection points among the three linear arrays by image matching; (3) listing three affine transformation formulas of fore sight, front sight and back sight of the three-linear array images; (4) establishing an error equation and solving to acquire corrections of affine transformation parameters, and revising the affine transformation formulas by utilizing the corrections; (5) listing the affine transformation formulas of the fore sight and the back sight again by utilizing camera parameters and the revised affine transformation formula of the front sight; and (6) solving the revised affine transformation formulas of the fore sight and the back sight in the step (4) and the listed affine transformation formulas of the fore sight and the back sight in the step (5) simultaneously to acquire the corrections of the camera parameters, and revising the camera parameters to improve and calibrate the block adjustment of the three-linear array three-dimensional satellite.

An indoor positioning method based on signal receiving strength indicator correlation includes the steps that correlation transformation of the signal receiving strength indicator is performed, and similarity calculation and positioning matching are performed on correlation fingerprint data. Through the technical scheme, the difference between terminals in a crowdsourcing mode can be overcome, and the stability and the positioning accuracy of an indoor positioning system are kept.

The invention discloses a Hadoop cloud platform-based Web resource personalized recommendation system and method. The system comprises a user model module which collects browsing behavior information of a user on a Web resource page, endows different weights to different behavior types of the user according to the browsing behavior information of the user on the Web resource page, and calculates the degree of interest of the user in the Web resource page according to the different weights, a recommendation algorithm module which carries out clustering processing on the collected browsing behavior information of the user on the Web resource page by using a recommendation algorithm according to the degree of interest calculated by the user model module, then constructs a matrix according to a clustering result, carries out nearest neighbor analysis on a target resource through the constructed matrix to obtain a nearest neighbor result of the target resource, and forecasts and fuses the degree of interest of the user according to the nearest neighbor result to obtain a recommendation scheme, and a parallel processing module which carries out off-line distributed calculation on the recommendation algorithm under a Hadoop cloud platform, and saving a calculation result.

The invention provides a high-speed rail turnout intelligent fault detection method based on hybrid deep learning. According to the method, a hybrid deep learning method combining deep learning automatic feature extraction and traditional machine learning abnormal point detection is applied to turnout fault detection method research; deep learning is used for automatic feature extraction, featuredata which are smaller in dimension and more abstract are formed, and the problems that the feature extraction process depends on human experience and a clustering algorithm is difficult to calculateunder high-dimensional data are solved; then a clustering algorithm is combined with expert knowledge to select a normal data cluster, and the problem that a large amount of labeled data cannot be obtained is solved; and finally, the data labeled to be normal is used to train a list classification support vector machine for the abnormal point detection, and the problem that no label exists or thelabel is insufficient in the turnout fault detection process is solved.

The invention discloses a graph centrality calculating method and device. The method includes sampling an original graph indicative of a network structure and acquiring nodes of part in sequential connection in the original graph and at least two sampling slave graphs composed of connection edges between the nodes; calculating the graph centrality separately for the sampling slave graphs and forming graph centrality calculating results of the corresponding sampling slave graphs; mapping the calculation results of the sampling slave graphs into the original graph and acquiring a mapping resultof sampling nodes in the original graph and a mapping result of non-sampling nodes in the original graph; clustering the mapping results corresponding to the sampling slave graphs and acquiring the centrality calculation result of the original graph. By applying the method provided by the invention, precision and efficiency of centrality calculation of the original graph specific to a network canbe improved.

The invention provides a power transmission and transformation project cost calculation system and a cost calculation method based on a three-dimensional model. The method comprises the following steps of 1, acquiring power transmission and transformation project three-dimensional design result data from a three-dimensional design platform and converting the data into cost project data; 2, carrying out engineering project division on the cost engineering data; 3, calculating the engineering quantity through the construction cost engineering data; 4, calculating engineering cost according to the engineering quantity through a preset quota mode, and achieving engineering general budget compilation; and 5, compiling an engineering quantity list, a bidding limit price and a bidding quotation according to engineering quantity pricing specifications through a list mode. Through standardized management of quota and inventory valuation data, the problems that in the traditional cost compilation process, the engineering quantity is difficult to calculate, errors are prone to occurring in manual price assembly and adjustment, cost data cannot be traced back, and compilation results are difficult to share and apply are solved.

The invention relates to a partly-observable automatic pilot decision-making method and system based on constraint online planning. The decision-making method is mainly used for automatic pilot, so that a vehicle is enabled to have various driving modes. The system comprises a driving environment state unit, a search unit, a simulation unit and a cost constraint unit. By the decision-making method, a driving scheme can be generated according to current driving environment and can be adjusted in real time according to real-time road conditions and vehicle conditions to improve flexibility of the scheme. A history-based Monte Carlo search tree is built by the method, and simulating of a solution is performed on the basis of real conditions, so that reliability is improved. In addition, the method meets certain optimal selection conditions to ensure acquired strategies to be random strategies, so that the defect of deterministic strategies is made up. The decision-making method completelymeets driving needs of current common users, and various modes are provided for selection especially, so that user experience is improved greatly.

The invention discloses a method and device for calculating the shielding effectiveness of an electromagnetic shielding net and a storage medium, and the method comprises the steps: obtaining a distribution model diagram of the electromagnetic shielding net with periodic distribution characteristics and uniform plane electromagnetic waves, and obtaining subunits of the electromagnetic shielding net; establishing a geometric model containing continuous periodic boundary conditions according to the subunits; dividing the geometric model into an electromagnetic shielding net space, an external space and a shielded space; placing the subunits in an electromagnetic shielding net space, placing the uniform planar electromagnetic waves in an external space, and presetting an observation point ina shielded space; setting simulation model parameters according to the geometric model and the electromagnetic shielding net to obtain a simulation model; performing numerical calculation on the simulation model to obtain the electric field intensity at the observation point; and obtaining the shielding effectiveness of the electromagnetic shielding net according to a shielding effectiveness calculation formula. The shielding effectiveness of the electromagnetic shielding net with the periodic distribution characteristic and any shape can be calculated, and the calculation result is more accurate.

The invention discloses an automobile speed measuring device and an automobile speed measuring and control method. The automobile speed measuring device comprises an automobile speed controller, a wheel deflection measuring instrument, a wheel speed measuring instrument and a gravity sensor. The speed and the deflection angle of two front wheels are measured in real time, the speeds, of moving around an instantaneous center, of the left front wheel and the right front wheel of an automobile generated when the automobile turns are converted into the moving speeds of the automobile center, the average value of the two automobile speeds at the automobile center serves as the actual running speed generated when the automobile turns, and meanwhile, the automobile speed can be controlled according to the stress condition of two front wheel shafts. According to the automobile speed measuring device and the automobile speed measuring and control method, the problems that in the prior art, the transmission box output shaft rotating speed is adopted to be converted into the automobile speed, the automobile speed accuracy is low when the automobile turns, the control accuracy is lowered, and the automobile running accidents are caused easily are solved effectively, and the device and the method have very high practical value.

The invention requests to protect a high-dimensional sparse text data clustering method based on Spark. The method comprises the following steps of: 1) a RDD (Resilient Distributed Dataset) is used for reading a dataset; 2) a RDD interface is used for designing a distributed spare vector set; 3) a similarity between the distributed spare vector set and an integral dataset of a node where the distributed spare vector set is positioned is calculated, similar matrixes are obtained by abstraction according to numbers, the stored similar matrixes are subjected to symmetrization, and the normalized forms and the Laplace matrix forms of the similar matrixes are obtained; 4) SVD (Singular Value Decomposition) is used for decomposing the normalized Laplace matrix in the 3); 5) a new matrix constructed in the 4) is used into a K-means model as a sample to be trained; and 6) an established model is used for clustering a test set. By use of the method, the operation performance of a traditional clustering algorithm under a big dataset is improved.

The invention discloses a method for measuring an electrified railway single-track bank section contact network lightning trip-out rate. The method comprises the steps of (1) obtaining electrified railway line parameters, (2) calculating the coordinates of lightning type dividing points and determining the influence areas of different lightning types of a contact network, (3) calculating an induction lightning trip-out rate, (4) calculating a back trip-out rate, (5) calculating a shielding failure trip-out rate, and (6) determining a total trip-out rate. The method is convenient to calculate, and a problem of the difficult calculation of the electrified railway lightning trip-out rate is solved.

The invention discloses a method for measuring an electrified railway double-track bank section contact network lightning trip-out rate. The method comprises the steps of (1) obtaining electrified railway line parameters, (2) determining the influence areas of different lightning types of a contact network and calculating the coordinates of single side lightning type dividing points, (3) calculating a double-track induction lightning trip-out rate, (4) calculating a double-track back trip-out rate, (5) calculating a double-track shielding failure trip-out rate, and (6) determining a double-track total trip-out rate. The method is convenient to calculate, and a problem of the difficult calculation of the electrified railway lightning trip-out rate is solved.

The invention discloses a two-dimensional calculation method for composite material layer cracking. The method comprises the following steps: S1, solving node displacement by adopting an extended finite element method; S2, based on the virtual crack closing technology, the energy release rate of the tip of the layer crack is obtained; S3, judging whether the layer cracks are expanded or not; and S4, when a predetermined load is reached or the structure fails, ending the calculation. The method has the advantages that the virtual crack closing technology and the expansion finite element method are combined, the expansion finite element is adopted for representing the layer crack, the unit boundary does not need to be matched with the crack shape when the grid is divided, and the grid does not need to be divided again when layer crack expansion is simulated; the requirement of layer cracking simulation on the unit size is reduced, and the calculation result is more accurate than that of a cohesion unit method; and when layer splitting expansion analysis is carried out, iteration is not needed, and the calculation efficiency is improved.

The invention discloses a method for measuring an electrified railway single-track elevated section lightning trip-out rate. The method comprises the steps of (1) obtaining electrified railway line parameters, (2) calculating the coordinates of lightning type dividing points and determining the influence areas of different lightning types of a contact network, (3) calculating an induction lightning trip-out rate, (4) calculating a back trip-out rate, (5) calculating a shielding failure trip-out rate, and (6) determining a total trip-out rate. The method is convenient to calculate, and a problem of the difficult calculation of the electrified railway lightning trip-out rate is solved.

The invention discloses a Natch risk calculation method and system for a coupling probability model and an information diffusion method, and belongs to the technical field of risk calculation of technical accidents caused by natural disasters. To solve the problem of insufficient Natch risk calculation accuracy of a large-scale region in the prior art, the invention provides a method, a coupling probability model and an information diffusion method for calculating the natural disaster probability, different types of Natch environmental event risks are analzyed by gridding, the equipment failure probability is calculated under the natural disaster and Natch risk probability is futher calculated. According to the risk calculation method, the problem of low Natch risk assessment precision of a large-scale region is solved, high-risk hot spot regions can be identified, priority management of Natch risks is realized, and a direction is provided for reducing the Natch risks.

The invention discloses a bandwidth prediction method and system for a power distribution communication service. The method comprises the steps of obtaining power distribution network operation state data collected by a plurality of sensor terminal devices arranged in a power distribution station; wherein the power distribution network operation state data comprises periodic service data and random service data; calculating a first bandwidth according to the operation state data of the power distribution network, the application layer transmission protocol overhead data and the data transmission delay; calculating a second bandwidth according to the first bandwidth, the number of the sensor terminal devices and the service concurrency rate; calculating a third bandwidth according to the second bandwidth; and determining the third bandwidth as the predicted bandwidth of the power distribution communication service. According to the invention, the bandwidth calculation is simplified, and the problem that the bandwidth calculation is difficult when the random service and the periodic service at the wireless gateway side coexist is solved.

The invention provides an audio processing method and device. The audio processing method comprises the following steps: acquiring a to-be-processed audio; preprocessing the to-be-processed audio to obtain a global frequency domain frame sequence corresponding to the to-be-processed audio; constructing an audio feature set corresponding to the to-be-processed audio according to the global frequency domain frame sequence, and inputting the audio feature set into an audio processing model for processing to obtain a local frequency domain frame sequence; and generating a target audio for replacing the to-be-processed audio based on the local frequency domain frame sequence and the global frequency domain frame sequence, thereby realizing accurate compensation of the to-be-processed audio, and effectively improving compensation efficiency and compensation accuracy.

The invention requests to protect a high-dimensional sparse text data clustering method based on Spark. The method comprises the following steps of: 1) a RDD (Resilient Distributed Dataset) is used for reading a dataset; 2) a RDD interface is used for designing a distributed spare vector set; 3) a similarity between the distributed spare vector set and an integral dataset of a node where the distributed spare vector set is positioned is calculated, similar matrixes are obtained by abstraction according to numbers, the stored similar matrixes are subjected to symmetrization, and the normalized forms and the Laplace matrix forms of the similar matrixes are obtained; 4) SVD (Singular Value Decomposition) is used for decomposing the normalized Laplace matrix in the 3); 5) a new matrix constructed in the 4) is used into a K-means model as a sample to be trained; and 6) an established model is used for clustering a test set. By use of the method, the operation performance of a traditional clustering algorithm under a big dataset is improved.

The invention provides an accelerated calculation method of a recurrent neural network. The accelerated calculation method comprises the steps of obtaining a parameter matrix multiplier of the recurrent neural network; dividing a multiplier and a multiplicand in the parameter matrix multiplier to obtain a two-dimensional matrix; inputting the two-dimensional matrix into a three-dimensional pulsation array according to a preset sequence; and determining an output result of the parameter matrix multiplication according to the three-dimensional pulsation array. According to the method and the device, the calculation time of large-size matrix multiplication is greatly shortened, and hardware resources required by calculation are reduced. The invention further provides an accelerated computingsystem of the recurrent neural network, a computer readable storage medium and a terminal, which have the above beneficial effects.

The invention discloses a novel traffic accident scene video speed measurement method based on four-point plane homography. The method is characterized in that the method comprises the following steps: 1, checking an accident scene video, so that the video is ensured to be intact and is an original video; 2, checking and recording the frame rate of the on-site video recording, and extracting all key frame photos in the video record by using video processing software; 3, respectively selecting four pairs of object points Pk (xk, yk) and corresponding image points Ik (uk, vk) on a space plane pi1 and a digital image plane pi 2 of the camera; 4, performing normalization processing on the object point Pk (xk, yk) and the image point Ik (uk, vk), wherein the coordinate of the processed point is PCk (xCk, yCk) and point ICk (uCk, vCk); 5, solving a vector h by using the normalized coordinate by using the formula (1), constructing a projection transformation matrix H by using the vector h, and calculating a matrix H-1. The invention relates to the field of detection equipment, in particular to a novel traffic accident scene video speed measurement method based on four-point plane homography. The vehicle speed can be conveniently determined.

The invention relates to a slope displacement prediction method based on a hybrid intelligent algorithm. The method comprises the following steps: collecting the monitoring information of a slope, and building a database according to the monitoring information; eliminating redundant information in the database; reducing the dimension of data in the database; making a training sample and a test sample according to the data in the database; establishing a model, and training the model through the training sample to obtain a first prediction model; correcting the first prediction model through the test sample to obtain a second prediction model; predicting the displacement of the target slope through a second prediction model and the real-time monitoring information of the target slope to obtain a first prediction value; and correcting the first predicted value through a Markov method to obtain a final predicted value. Redundant information in the collected information is eliminated, and dimensionality reduction is carried out on the redundant information, so that the prediction result is more accurate, and the calculated amount is lower. And the slope displacement is predicated through a least square support vector machine and a particle swarm optimization algorithm.

The invention discloses a model construction method and device and a concrete compressive strength prediction method and device. The model construction method comprises the following steps: establishing a database about cubic concrete compressive strength influence factors; preprocessing the data in the database; constructing a prediction model through an adaptive neural fuzzy inference system; and training a prediction model by using the preprocessed data to obtain a prediction training model, wherein the prediction training model is used for concrete compressive strength prediction. The concrete compressive strength can be efficiently and accurately predicted in a short time, and the method can be applied to artificial sand concrete and also can be applied to common concrete.

The invention provides a big data-based court line loss data deep mining analysis method. The method comprises the steps of obtaining historical power data of a watt-hour meter in a court and power characteristic attributes of the court where the watt-hour meter is located; integrating the power data according to different buses, and calculating the power line loss of the buses according to different types of line loss models by the integrated power data; classifying the courts according to the power characteristic attributes of the courts where the buses are located, and training a court line loss fitting model by combining the power line loss of each bus in the courts; and calculating the real-time electric quantity unbalance rate of each bus, judging the transformer area where the fault bus is located, obtaining the electric power characteristic attribute of the current transformer area, taking the electric power characteristic attribute as the input of the transformer area line loss fitting model, and calculating the electric power prediction line loss of the current bus. According to the invention, through carrying out analysis and research on the bus watt-hour meter power data under the background of big data, the technical problems of difficult analysis and calculation of a large number of transformer area line loss rates, indefinite line loss rate control targets, incapability of judging transformer area line loss rate abnormity and the like in the prior art can be effectively solved.

The invention discloses a hydrogen consumption calculation method and device, terminal equipment and a storage medium. The method comprises the steps: obtaining respective residual hydrogen mass of a hydrogen fuel cell system at a t moment and a (t+k) moment; calculating the driving mileage of the vehicle where a hydrogen fuel cell is located from the t moment to the (t+k) moment; calculating the initial hydrogen consumption per hundred kilometers at the (t+k) moment according to the residual hydrogen mass at the t moment, the residual hydrogen mass at the (t+k) moment and the driving mileage; and carrying out weighted correction on the initial hydrogen consumption per hundred kilometers at the (t+k) moment according to the real hydrogen consumption per hundred kilometers at the t moment to obtain the real hydrogen consumption per hundred kilometers at the (t+k) moment, wherein when t is 0, the real hydrogen consumption per hundred kilometers at the t moment is the preset reference hydrogen consumption. By adopting the method and the device, the technical problems of difficulty in hydrogen consumption calculation and low accuracy in the prior art can be solved.

The invention discloses a vibration isolation structure of a shaftway lower-hanging type converting bracket and a construction method of the vibration isolation structure. The vibration isolation structure is characterized in that a converting beam of an out-extending bracket is arranged on the periphery of a shaftway or a core cylinder, the top elevation of the converting beam is flush with the top elevation of a basement top plate, a basement hanging structure of the shaftway or the core cylinder is arranged below the converting beam, the converging beam is arranged on a vibration isolation supporting base through the bracket, vibration isolation grooves are formed between the basement hanging structure, a basement bottom plate and a basement stand column, and specific construction comprises underground layer construction and hanging and vibration isolation layer structure construction. Compared with the prior art, the basement hanging structure of the shaftway or the core cylinder and a structure below a vibration isolation layer are complete separated, vibration isolation effect is good, the problem that a vibration isolation seam is too wide, shaftway vertical aligning cannot be achieved, and the shaftway or a core cylinder side supporting base is pulled is effectively solved, structure stability is good, the vibration isolation support is flexibly arranged in different structure systems, and construction mounting and later-period maintaining are convenient.

The invention provides a knee joint X-ray image quality control method and device. The method comprises the following steps: acquiring a normal position X-ray image and a lateral position X-ray image of a knee joint; detecting key points for positioning and eliminating shielding influence at the front position and the side position of each bone of the knee joint; and carrying out image segmentation based on the key points, scoring each quality control index by calculating related parameters representing the quality control indexes, and summarizing to obtain a quantitative score of the knee joint X-ray image. According to the method, a certain number of key points are marked at the normal position and the lateral position of each bone of the knee joint, so that positioning of each bone on the knee joint can be conveniently realized, and calculation of quality control indexes is simplified; due to the fact that segmentation, classification and other operations involved in quality control index calculation are carried out based on key points, most background areas can be removed, the memory occupation amount and the data calculation amount are reduced, and the running speed is increased; by setting a small number of redundant key points, the problem that quality control parameters are difficult to calculate due to shielding can be effectively solved.

The invention discloses a method for measuring an electrified railway single-track elevated section lightning trip-out rate. The method comprises the steps of (1) obtaining electrified railway line parameters, (2) calculating the coordinates of lightning type dividing points and determining the influence areas of different lightning types of a contact network, (3) calculating an induction lightning trip-out rate, (4) calculating a back trip-out rate, (5) calculating a shielding failure trip-out rate, and (6) determining a total trip-out rate. The method is convenient to calculate, and a problem of the difficult calculation of the electrified railway lightning trip-out rate is solved.

The invention discloses a method for measuring an electrified railway double-track bank section contact network lightning trip-out rate. The method comprises the steps of (1) obtaining electrified railway line parameters, (2) determining the influence areas of different lightning types of a contact network and calculating the coordinates of single side lightning type dividing points, (3) calculating a double-track induction lightning trip-out rate, (4) calculating a double-track back trip-out rate, (5) calculating a double-track shielding failure trip-out rate, and (6) determining a double-track total trip-out rate. The method is convenient to calculate, and a problem of the difficult calculation of the electrified railway lightning trip-out rate is solved.