152results about How to "Calculation speed" patented technology

This invention discloses a method and system for predicting the dissolution time of bubbles in oil-paper insulation, belonging to the field of electrical equipment insulation technology. The method includes: calculating the amount of various gases dissolved per unit volume of oil after the gas dissolution in oil reaches equilibrium; using 47℃ as a reference temperature, extrapolating this data to other temperatures and solubilities using the Arrhenius relation, and calculating the diffusion coefficients of various gases in oil at each temperature; calculating the diffusion coefficients of various gases in paper at each temperature; establishing a prediction model for the dissolution time of bubbles in oil-paper insulation, and calculating the dissolution time. This invention considers three factors—gas solubility in oil, diffusion coefficient in oil, and diffusion coefficient in paper—that vary with temperature and gas type, and calculates the dissolution time of a single stationary bubble in oil-paper insulation. This effectively reduces the risk of bubbles during transformer operation and can be widely applied to oil-paper insulation systems with the same kinetic description.

The application provides a kind of tower type solar thermal power station mirror field optical efficiency calculation method, belongs to tower type solar power field, solve mirror field optical efficiency large amount of calculation and low precision problem.Its technical scheme is: including the following steps: S1: obtaining mirror field parameters and solar position;S2: determine the atmospheric transmission efficiency and cosine efficiency of each heliostat;S3: calculate the shadow blocking efficiency of each heliostat;S4: calculate the truncation efficiency and optical efficiency of each heliostat.The beneficial effects of the application are: a kind of tower type solar thermal power station mirror field optical efficiency calculation method is designed, the method is gridded to the current heliostat, and the grid point and its adjacent heliostat vertex are projected to the mirror field horizontal plane along the incident light direction or the reflected light direction to calculate the shadow blocking efficiency, the heliostat that may be blocked is selected within a reasonable range, which greatly reduces the calculation amount of heliostat shadow blocking rate, and the operation accuracy is high, the method can ensure the calculation accuracy while improving the calculation speed of tower type solar power station mirror field optical efficiency simulation.

The application discloses a driver fatigue behavior detection method and system based on video posture invariance, and relates to the technical field of computer vision. The application proposes a key frame selection model based on facial geometric information and a head and face action information fusion space-time network. First, the driver video captured by the vehicle-mounted camera is subjected to sequential processing, and image preprocessing is performed. Then, the key frame selection model based on facial geometric information is constructed based on the geometric features of the facial key points and a two-stage decision mechanism, and the key frames in the video sequence are extracted. Finally, the facial action modalities under any posture are extracted based on the facial forward processing, and the head posture attributes obtained based on the head posture estimation are combined to construct the head and face action information fusion space-time network, which is used for detecting the yawning, speaking, normal and other driver states. The application fully considers the head posture attributes, has high posture robustness, and can effectively distinguish the yawning and other fatigue behaviors from other driver states.

The application discloses a power grid power transmission section identification method based on an improved PageRank algorithm and network conversion, first, an improved PageRank algorithm is used to calculate a node criticality index, and a network constraint coefficient is calculated according to a network topological structure and is used to correct a restart vector; second, a key power transmission section search topological graph is constructed through network conversion and node criticality index calculation, and identification of the key power transmission section is converted into search of a shortest path; finally, according to energy transaction between a generator node and a load node, a search range of the key power transmission section is set, a related cut set with the highest criticality is found, and through checking and correction, an identification result of the key power transmission section is obtained. The application can well improve the deficiency of the original PageRank algorithm when applied to the power grid, can accurately identify the power transmission section of the power grid, provides a key monitoring guide for power grid monitoring and dispatch personnel, and better guarantees the safe and stable operation of the power grid.

This application relates to a method, apparatus, device, and medium for adaptive parameter pruning of a deep learning model. The method includes: acquiring a pre-set training set of a deep learning model; pre-training the deep learning model based on the training set to determine the parameters of each layer in the deep learning model; capturing the low-rank structure of the parameters of each layer in the deep learning model based on a unified tensor network model, and decomposing the parameters into multiple ring factor tensors and a core factor tensor; constructing an adaptive parameter pruning model based on a sparse Bayesian learning algorithm according to the prior distribution results of the Gaussian distributions corresponding to the ring factor tensors and the core factor tensor, and the prior distribution results of the Gamma distribution corresponding to the noise level; and pruning irrelevant components of the parameters based on the adaptive parameter pruning model that has reached convergence to determine the corresponding compressed parameters. This application enables the deep learning model to maintain a low number of parameters while still maintaining high accuracy.

This invention discloses a simplified calculation method for determining the length of anchorless sheet piles in bank protection reinforcement, belonging to the field of foundation pit support technology. The method includes the following steps: Step S1, performing pile length sensitivity analysis to examine the influence of the total sheet pile length L, soil cohesion c, and internal friction angle on the pile top displacement; Step S2, establishing a pile top displacement prediction formula based on the passive earth pressure coefficient theory and fitting multiple sets of working condition data; Step S3, establishing a simplified design process of "parameter determination - formula inversion - optimization verification," using the pile top displacement prediction formula to invert and solve for the optimized pile length based on soil parameters and the maximum pile top displacement, and comparing it with the minimum pile length that satisfies kick-toe stability to determine the final design pile length. This invention simplifies "trial calculation iteration" to "precise inversion," improving the design efficiency of anchorless sheet piles and achieving a balance between safety and economy by quantifying the influence of parameters.

The invention relates to a regional ocean sound propagation field millisecond-level prediction method based on a deep neural network, and the method comprises the steps: constructing two factors which have the greatest influence on a sound propagation field for a target region: a sound velocity profile and a terrain; an environment information data set of an area sound propagation field is constructed by constructing an annual sound velocity profile of a target area node and building a large number of two-dimensional terrain models by extending around the node by 360 degrees, and the sound propagation fields of the area under different environment conditions are calculated by using a traditional sound field calculation model to serve as a training set; feature simplification parameters of the sound velocity profile and the terrain in each environment sample are extracted to serve as guide information of a corresponding sound propagation field, and a deep neural network is built to learn distribution rules of the sound propagation fields under different environment conditions in the area; and finally, the trained model can quickly predict the corresponding sound propagation field in the region according to different environmental condition characteristic batches.

The application discloses a wind-solar-water combined short-term optimization scheduling method based on wind-solar uncertainty prediction scenes, which comprises the following steps: pre-processing and dividing levels of historical wind-solar output data; establishing a wind-solar prediction scene construction model and a reduction model to generate a wind-solar prediction typical scene set; establishing a scene migration probability calculation model to obtain the migration probability between different time periods of actual wind-solar output values; updating the prediction scene according to the wind-solar prediction typical scene set, combining the hourly wind-solar output measured data and the scene migration probability information; and performing wind-solar-water combined short-term optimization scheduling. The application is based on the prediction and measured data of historical wind-solar output, considers the influence of the uncertainty of wind-solar output on the wind-solar-water combined short-term optimization scheduling by constructing the wind-solar output prediction scene, and provides a new method and technology for the optimization scheduling of a multi-energy complementary system connected with new energy.

This application discloses a method, apparatus, device, and computer-readable storage medium for image foreground segmentation, particularly relating to foreground segmentation of circular regions in images displaying circular regions of different colors. The method establishes a labeled information database based on the circular region image with labeled information, processes the information to obtain a threshold range corresponding to the target pixel region in the color space, and then accurately separates the monochrome circular particle foreground images from the user-provided image based on this threshold range. Each monochrome particle image is segmented separately to obtain monochrome particle foreground segmentation results. Finally, the multiple monochrome circular particle foreground segmentation results are fused to obtain the foreground segmentation result of the multi-dimensional circular region image. This method has higher recognition efficiency and better noise tolerance compared to the traditional watershed algorithm.

This application relates to a report generation method, apparatus, computer equipment, storage medium, and computer program product. The method includes: obtaining a report configuration corresponding to an initial report; the report configuration includes a data source identifier, a first operator sequence, and a second operator sequence; obtaining a query field; obtaining initial data from the data source corresponding to the data source identifier based on the query field; performing calculations on the initial data based on the first operator sequence to obtain intermediate data; aggregating the intermediate data to obtain an intermediate table; and performing calculations on the intermediate table based on the second operator sequence to obtain a target report containing the query field. This method improves the flexibility of reports. This invention can be applied to various systems involving reports, such as enterprise management systems and Enterprise Resource Planning (ERP) systems.

The application discloses a building energy consumption real-time control method based on DQN, generates multiple groups of scenes based on building photovoltaic power generation and building rigid load prediction data, optimizes building energy consumption behavior by using a DQN algorithm, reasonably designs a DQN reward function by using the concept of random optimization, and makes the optimization result have good robustness to the uncertainty of building power generation and load. The application makes the building maintain high comfort all day, increases the flexibility of building energy consumption by scheduling energy storage equipment, and reduces the building energy consumption cost.

This invention relates to a method for predicting sandbar response under the combined effects of water level and waves, considering hysteresis, during storms. The method includes: starting with initial sandbar morphology data of a target shoreline during a specific storm event, using a sandbar evolution model considering hysteresis to predict sandbar response, and outputting a predicted curve of sandbar position change throughout the storm process. The sandbar evolution model calculates storm dynamic forcing based on historical storm hydrodynamic data, establishes a model relating relaxation timescales to storm dynamic forcing, obtains a relaxation timescale characterizing the hysteresis characteristics of the sandbar response, and establishes a model relating the sandbar equilibrium position to hydrodynamic forcing based on historical hydrodynamic forcing data and sandbar position changes, obtaining... t The sandbar equilibrium position at any given moment. This invention can more accurately simulate sandbar behavior throughout the entire storm process, especially during the attenuation phase, significantly improving the practicality and reliability of sandbar response prediction throughout the entire storm process (including the aftereffect period).

The application is suitable for the field of network communication technology, and provides a network congestion processing method and device, a terminal, a medium and a program product. The method comprises the following steps: acquiring a round-trip time sequence of a target network transmission link; performing differential processing on the round-trip time data at adjacent time instants in the round-trip time sequence to obtain a plurality of high-frequency components in the round-trip time sequence; updating the round-trip time data at even time instants in the round-trip time sequence according to the shift results of the plurality of high-frequency components to obtain a plurality of low-frequency components in the round-trip time sequence; identifying the network state of the target network transmission link according to the plurality of high-frequency components and the plurality of low-frequency components; and performing a first window reduction operation on the congestion window of the target network transmission link in the case that the network state is a network congestion state. The method can quickly and accurately distinguish the network jitter and congestion problems in a network environment with high bit error rate and high jitter, thereby improving the data transmission efficiency.

The present specification relates to the technical field of fiber-optic gyroscopes, and discloses a reciprocal structure 3x3 fiber-optic gyroscope and a use method thereof, to solve the problems of poor anti-interference, complex calculation, and no consideration of the influence of temperature change on test results, poor environmental adaptability, and limited practicality of a typical non-reciprocal structure 3x3 fiber-optic gyroscope; three photoelectric detectors, a 2x2 coupler, a 3x3 coupler, a temperature sensor, and a signal processing module; the 2x2 coupler, the 3x3 coupler, the first photoelectric detector D1, the second photoelectric detector D2, and the third photoelectric detector D3 constitute a reciprocal structure 3x3 fiber-optic gyroscope optical path. The reciprocal structure 3x3 fiber-optic gyroscope extracts a reciprocal end output signal, uses the reciprocal end output signal as a reference quantity for differential demodulation, eliminates the influence of optical path loss variation and light source fluctuation on the gyroscope output signal, and greatly improves the anti-interference of the gyroscope system.

The application discloses a ranging method and system, wherein the method comprises the following steps: acquiring a ranging image; acquiring a contour in the ranging image, determining a plurality of positioning points according to the contour, and obtaining a positioning point cluster; determining a two-dimensional code in the ranging image according to the positioning point cluster; determining a three-dimensional coordinate of the two-dimensional code in a camera coordinate system according to the two-dimensional code and an internal parameter matrix and a distortion matrix of the camera; converting the three-dimensional coordinate of the two-dimensional code to a world coordinate system, and determining a vertical distance from the two-dimensional code to a plane formed by an xy axis group of an origin of the world coordinate system. The method has the advantages of high ranging accuracy, simple method, fast calculation speed and wide application range. The system has the advantages of low ranging cost, low cost and low technical risk, and can provide accurate ranging with low error.

The application discloses a batch processing matrix multiplication optimization implementation method and system for a heterogeneous processor, and the method comprises the following steps: in step S1, a CPU allocates space for a matrix on a shared DDR memory through an hthread_malloc function; in step S2, the CPU calculates decision parameters through a decision algorithm, wherein the decision parameters comprise a matrix block size parameter and an m_batch size parameter; and in step S3, a DSP function is started based on the decision parameters. The application has the following beneficial effects: a high-efficiency batch processing matrix multiplication (BGEMM) algorithm is implemented on a DSP, and the BGEMM algorithm can effectively accelerate multi-field applications including deep learning; the memory access delay of a DSP calculation unit is reduced, the calculation speed is improved, and the calculation efficiency of GEMM is improved.

The application discloses a voltage stability margin calculation method considering a control mode of a photovoltaic power station, and adds modeling consideration of a reactive power-voltage control mode of the photovoltaic power station in voltage stability margin calculation. In a continuous power flow calculation process, parameterized power flow equations are obtained by extending node voltage with the fastest voltage drop. Meanwhile, in a prediction link in the continuous power flow calculation process, a hybrid prediction method is adopted. In the case that voltage margin is large, nonlinear prediction is adopted to accelerate the calculation speed. In the case that voltage stability limit is approached, linear prediction is adopted. The voltage stability margin calculation method fully considers the influence of the control mode of the large-scale photovoltaic power station on voltage stability, and improves the calculation efficiency and the calculation precision.

The invention relates to a potential function model construction method, system and device and a storage medium, and belongs to the technical field of digital data processing. The construction method of the potential function model comprises the following steps: determining a neighbor atom set for each atom in an atom system according to a truncation radius; constructing a neighbor environment descriptor of each atom based on the neighbor atom set of each atom; inputting the neighbor environment descriptor of each atom into an independently configured neural network corresponding to the element type of the neighbor environment descriptor, and performing forward propagation calculation from an input layer to an output layer of the neural network to obtain energy of each atom; performing back propagation calculation and descriptor back calculation on the energy of each atom from the output layer to the input layer through each independently configured neural network to obtain partial derivative of the energy of each atom to the position of the adjacent atom; and obtaining the stress of each atom based on the partial derivative of the energy of each atom to the position of the adjacent atom. The potential function model has both precision and calculation efficiency.

The invention provides a cell initial access-oriented multi-dimensional parameter estimation method and system, a medium and a receiver. The method comprises the following steps of: receiving a received signal from at least one network node; generating a local reference signal corresponding to each network node signal; for each network node signal, acquiring a rough candidate parameter area based on the received signal and a corresponding local reference signal in a preset multi-dimensional parameter space; and in the rough candidate parameter region, searching the first-dimensional parameter based on a divide-and-conquer method to obtain a matched multi-dimensional parameter estimation value. According to the cell initial access-oriented multi-dimensional parameter estimation method, the cell initial access-oriented multi-dimensional parameter estimation system, the medium and the receiver, high-precision estimation of multi-dimensional parameters can be quickly and efficiently completed.

A synaptic transistor device with a wide spectral response is characterized by comprising: a floating gate layer for capturing light signals of different wavelengths to generate photogenerated carriers; a tunneling layer disposed on the floating gate layer for storing photogenerated carriers generated within the floating gate layer; an active layer disposed on the tunneling layer for transporting charge carriers and photogenerated carriers; source and drain electrodes, two source and drain electrodes symmetrically disposed on the active layer for providing a driving voltage to drive the flow of charge carriers and photogenerated carriers in the active layer, forming a circuit; a substrate disposed at the bottom of the synaptic transistor device for serving as the basis for fabricating the synaptic transistor device; a gate electrode disposed on the substrate for inducing charge carriers in the active layer; and an insulating layer disposed between the gate electrode and the floating gate layer for isolating the gate electrode and the active layer from conduction, wherein the floating gate layer is made of quantum dots with a wide spectral response, and a heterojunction structure is formed between the floating gate layer and the active layer.

This application discloses an image processing apparatus, method, device, and readable storage medium. The apparatus includes: a display screen for displaying a rendered image after medical image rendering processing; and a processor electrically connected to the display screen, the processor including: a graphics processor for acquiring the texture color value of a target pixel within a measurement region in the rendered image; and a central processing unit for acquiring a first pixel value corresponding to the texture color value based on a preset color scheme of the rendered image; and determining the CT value of the target pixel in the medical image based on preset window parameters and the first pixel value. In embodiments of this application, based on the measurement function of medical images, a GPU is used to calculate and render the medical images, maximizing the advantages of the GPU's fast parallel image calculation, saving CPU resources, and achieving the purpose of improving calculation speed and accelerating device response speed, thereby improving the smoothness and operational efficiency of the device.

The invention relates to the technical field of oil and gas exploitation, and particularly discloses an unconventional fracturing complex fracture network expansion simulation method based on uncertainty modeling, and the method comprises the steps: calculating the half length of a fracture based on perforation reservoir parameters obtained through logging and other methods in combination with an analytical fracture expansion simulation method; carrying out complex fracture network simulation by taking the fractal probability as a constraint and combining with a fracture growth algorithm with fractal probability random distribution characteristics to obtain reservoir complex fracture network form distribution; and finally, fracturing construction monitoring pump pressure fitting is performed based on an intelligent algorithm, the volume and form of the complex fracture network are inversely corrected, and real-time expansion simulation of the complex fracture network in the fracturing process is realized. According to the method, the complex branch fracture form can be depicted more finely, meanwhile, the calculation speed is high, and real-time simulation of the complex fracture net form in the fracturing process can be achieved; the method achieves the efficient and accurate simulation of the fracture form under the condition that the crustal stress of the reservoir is not clearly known, and provides an effective technical means for the fracturing construction design of the unconventional reservoir.

The application discloses a deep learning point target denoising method for remote sensing task planning and belongs to the technical field of mega remote sensing constellation task planning.The application is aimed at the problem that the existing method is slow in calculating the satellite imaging time window for a point target in mega remote sensing constellation task planning and is time-consuming.The application comprises the following steps: orbit recursion is carried out according to the initial coordinate position of each satellite to determine the eccentric anomaly and the true anomaly at a given time;the orbit six numbers and the longitude, latitude and height of the task target are expressed as coordinate vectors in the ECEF coordinate system to calculate the imaging side swing angle;the golden section search method is adopted to determine the imaging time window;the imaging time and the imaging side swing angle are compared to determine the mark L and obtain a training data set;the point task denoising neural network is trained by using the network training data set, and the cross-entropy loss function is used for training and fitting of the neural network to obtain the trained point task denoising neural network.The application is used for point target denoising in remote sensing task planning.

The application discloses a kind of flat wire winding permanent magnet synchronous motor hybrid thermal network modeling method, belong to the temperature field analysis design technical field of permanent magnet synchronous motor.Utilize two-dimensional finite element analysis software FEMM to calculate the detailed temperature distribution in the radial direction of motor, again in combination with thermal resistance network method to winding end space area and motor axial heat dissipation are modeled, on the basis of guaranteeing calculation precision, reduce model calculation time.The application has the advantages of high calculation precision, high calculation efficiency, compared with three-dimensional transient field finite element analysis, the model has faster calculation speed.On the other hand, compared with the traditional lumped parameter network model, the model can show more abundant permanent magnet motor temperature field results.

The application provides a natural language-based public opinion tracking method and device, obtains original Internet public opinions including multiple public opinion samples according to a target theme; obtains a label list of each public opinion sample based on a label library corresponding to the target theme and a title and content of each public opinion sample; classifies the label list of each public opinion sample based on the label library and a keyword corresponding to the target theme, and obtains first-class samples associated with the target theme; labels corresponding key information labels for each first-class sample based on the label library, and obtains an event occurrence time of each first-class sample; aggregates all first-class samples into one or more public opinion sets according to the key information labels and the event occurrence time, and tracks public opinions of each public opinion set. The application can alleviate the problems of key information loss and complex processing in the existing public opinion processing mode.

The application discloses a radio data processing method, device and equipment and a storage medium. The method comprises the following steps: acquiring a radio data set to be processed; wherein the radio data set to be processed has corresponding generation time information, the radio data set to be processed comprises radio data to be processed of a to-be-processed task, and the to-be-processed task comprises a plurality of subtasks executed in a preset order; determining a target memory matched with the radio data set to be processed according to the generation time information, a total number of memories and a pre-established memory management table, and storing the radio data set to be processed in the target memory; calling a processing thread corresponding to each subtask in the plurality of subtasks in the target memory according to the preset order, and processing the radio data to be processed by using the processing thread. The technical scheme of the application improves the calculation speed of radio data.

The application discloses a kind of fast simulation method of ion scattering track cluster driven by bypass factor, including constructing basic radiation parameter table;According to the basic radiation parameter table, search discrete energy point, form lookup table;According to the variation of bypass factor of each discrete energy point in lookup table, determine the scattering angle distribution characteristic parameter corresponding to discrete energy point, and generate scattering angle sample based on scattering angle distribution characteristic parameter and store in scattering angle sample pool;Initialize three-dimensional grid, set the initial position and direction of incident ion;Carry out incident ion transport and energy deposition cycle;Parallel execution of the simulation task of multiple incident ions, respectively in independent three-dimensional grid copy incident ion transport and energy deposition cycle, finally all three-dimensional grid copy is obtained Total dose distribution.This method achieves optimal balance between calculation efficiency and accuracy, so as to generate high-fidelity three-dimensional dose distribution chart.

This invention belongs to the field of vehicle automation technology, specifically relating to a machine vision-based method for locating target objects in vehicles. A camera on the vehicle acquires scene images of the target object, a neural network is used to obtain the center pixel coordinates of the target object in the scene image, and the distance between the vehicle and the target object is calculated using the camera's height from the ground and the camera's intrinsic parameter matrix. This invention is mainly applied to target object location during the retrofitting of old vehicles. By combining target object identification in a neural network with camera homography calibration, the relative position of the target object in 3D space is accurately calculated, enabling precise target object location even in complex scenes.

The application discloses a near-field equivalent method, a mask 3D near-field correction method and a storage medium. The optical near-field equivalent method comprises the following steps: setting a three-axis coordinate system, so that the initial propagation direction of a light source with a preset angle is propagated along a first axis, and an initial wave vector and a polarization direction are obtained; performing a rotation operation on the initial wave vector and the polarization direction according to the preset angle of the light source; analyzing the polarization direction and the polarization vector of the light source after the rotation operation; rotating the polygon and the light source according to the preset angle of the polygon, so that a new wave vector and a corresponding rotation angle are obtained; analyzing the polarization direction, the polarization vector and the component of the light source after the second rotation operation; generating an edge near-field under equivalent conditions by using a 2D strict electromagnetism algorithm; and performing linear superposition according to the respective components based on the polarization direction of the light source after the second rotation operation, so that an equivalent near-field result is obtained. The application reduces the calculation burden of near-field generation.

The invention discloses a self-adaptive control method for intelligent mixing of roadbed solidified soil at an open cut tunnel section of a tunnel, relates to the technical field of road roadbed engineering construction, and solves the technical problems that an existing roadbed solidified soil mixing effect evaluation method lags behind time and highly depends on manpower. According to the method, the mixing state of the road mixer can be quickly and effectively judged by utilizing the characteristic that the gray difference between the curing agent and the soil matrix is large, calculating the global gray value of the image of the mixing area and preliminarily evaluating the mixing effect, and meanwhile, the gray-scale map of the mixing area is divided into the curing agent area and the soil matrix area by adopting an OSTU binarization segmentation method, so that the mixing effect of the road mixer is improved. Based on the distribution characteristic of the image gray scale, the curing agent can be effectively separated from the soil matrix. The image is divided into a plurality of windows, the proportion of the curing agent in each window is counted, and the variance of the proportion of the curing agent in all the windows is calculated, so that the distribution uniformity of the curing agent among the windows can be effectively judged, and the mixing effect can be effectively evaluated.