14results about How to "Effective calculation" patented technology

The present application relates to a kind of intelligent algorithm of reheater brush coating area, by the calculation of the actual deviation temperature rise Δt sjpc Of superheated tube, reheater steam temperature rise deviation Δt, superheated tube wall temperature deviation Δt sdpc , superheated tube control temperature rise Δt kzpc , the temperature rise Δt jsws That needs to control reduction, establishes the correlation of spraying coefficient, realizes the effective calculation of brush coating area.The algorithm of the present application is based on the principle of heat conservation, since the temperature of working medium in reheater heating surface is equal to the average value of the tube wall temperature of this section heating surface, therefore, in the heating surface where superheating occurs frequently, brush coating can make the wall temperature of superheating area and the outlet steam temperature approach, so as to achieve the purpose of balancing the heat absorption of corresponding tube row.Through the calculation of brush coating area, it can effectively prevent the phenomena such as superheating of the last stage reheater and screen reheater tube wall, at the same time, save wear-resistant materials and improve economic efficiency.

The invention provides a turbine blade radiation temperature measurement error correction method based on a reflected radiation model, and the method comprises the steps: building a reflected radiation model through considering the reflected radiation of a preceding-stage guide blade, an adjacent moving blade and a combustion chamber wall surface at the same time; calculating a radiation angle coefficient between turbine blades and a radiation angle coefficient between wall surfaces of a combustion chamber by using a surrounding channel integral method; temperature distribution of the surface of the turbine blade and the wall surface of the combustion chamber at different temperatures is obtained through comsol simulation; and designing a multiple reflection radiation formula, accurately calculating the reflection radiation of the hot end part, and obtaining a corrected radiation temperature measurement result through inversion. According to the method, reflection radiation of a front-stage guide vane, adjacent movable vanes and a combustion chamber wall surface is considered, a surrounding channel integral method is adopted, surface element shielding between blade surfaces is considered, and a turbine blade surface radiation angle coefficient is calculated; the wall surface of the combustion chamber is increased, the reflection radiation of the hot end component is considered, a multiple reflection radiation formula is designed, the accurate reflection radiation of the hot end component is obtained, the reflection radiation calculation error is reduced, and the turbine blade radiation temperature measurement precision is improved.

This invention provides a depth prior-guided multi-view stereo 3D reconstruction method. It acquires N original images of the target to be reconstructed from different perspectives; it iterates through the N original images, using each image as a reference image and the remaining N-1 images as source images, inputting them into a trained depth map generation network to obtain N depth maps; and then fuses these N depth maps to obtain a 3D point cloud of the target to be reconstructed. Based on its network architecture, the subsequent depth map generation module uses the depth maps output by the preceding depth map generation module to generate a depth prior. This depth prior guides the Transformer in the subsequent depth map generation module to complete cost volume aggregation, generating the final depth map, thereby improving the final quality of the depth map and point cloud.

The application provides a bypass valve element modeling method based on Flowmaster, which comprises the following steps: determining a bypass valve structure, which comprises a control cavity spring and a working cavity spring; obtaining displacement amounts of a control cavity valve and a working cavity valve when the bypass valve is in a working state; solving the displacement amounts to obtain a bypass valve opening degree; establishing a linear equation of a bypass valve interface element according to a general linear equation of a multi-interface element; obtaining the opening degree and pressure of the bypass valve according to physical and geometric characteristic parameters of the bypass valve, the valve interface linear equation and inlet and outlet geometric parameters; obtaining the opening degree and pressure of the bypass valve according to a characteristic relation curve of the opening degree and the difference between a valve control end and an inlet pressure, or a characteristic relation surface of the opening degree and the control end pressure and the inlet and outlet pressure difference, the bypass valve interface linear equation and the inlet and outlet geometric parameters; and carrying out bypass valve element opening degree test and flow characteristic test to verify the correctness of the bypass valve element.

The present application relates to a circuit for performing neural network calculations on a neural network, the circuit comprising: a systolic array comprising a plurality of cells; a weight extraction unit, for each of a plurality of neural network layers, the weight extraction unit being configured to: for the neural network layer, send a plurality of weight inputs to cells along a first dimension of the systolic array; and a plurality of weight sequencer units, each weight sequencer unit being coupled to a different cell along the first dimension of the systolic array, for each of the plurality of neural network layers, the plurality of weight sequencer units being configured to: for the neural network layer, transfer a plurality of weight inputs to cells along a second dimension of the systolic array in a plurality of clock cycles, and wherein each cell is configured to calculate a product of an activation input and a corresponding weight input using a multiplication circuit.

The application relates to the technical field of oil and gas exploration, and discloses a strike-slip fracture identification method and device based on a variety of seismic attribute fusion technologies. The method first acquires seismic data and geological background data of a target area, then performs seismic profile interpretation and calculates coherence, curvature, tensor thickness, symmetry illumination attribute and maximum likelihood attribute based on the geological background and the seismic data, and finally determines the preferred attributes according to the scale of the fracture and performs quantitative fusion to identify the strike-slip fracture zone. The method is suitable for fractures of different scales, different attributes are selected for quantitative fusion, the advantages of different characteristics are complemented, the identification precision and sensitivity of the fracture are improved, and the research work of researchers in the aspects of the fracture and well position is effectively assisted.

The application discloses a hydraulic fracture geometric parameter nonlinear inversion method, which comprises the following steps: a strain analytical model is established according to preset mechanical parameters; a fracture parameter nonlinear inversion model is established according to the established strain analytical model and axial strain observation data obtained by a low-frequency DAS system under a neighboring well monitoring scene; a global optimal inversion is solved by using a nonlinear least square iteration algorithm according to preset constraint conditions and the fracture parameter nonlinear inversion model, and a fracture height, a fracture height asymmetric offset and a net pressure are obtained by inversion; and a maximum fracture width is calculated according to the obtained fracture height and net pressure.

The application relates to the field of robot control and discloses a live working robot load gravity compensation method, equipment and a storage medium, the method comprises the following steps: constructing a gravity compensation model according to the position relationship of each joint of a robot system; designing an installation angle optimization model with error sum of squares as a constraint index based on the installation angle of a force sensor; collecting multiple sets of mechanical arm pose and force data under experimental conditions; solving the installation angle optimization model by using the multiple sets of mechanical arm pose and force data under the experimental conditions based on an improved PSO method to obtain the optimal installation angle of the force sensor; installing the force sensor through the optimal installation angle, and performing gravity compensation on the mechanical arm end through the gravity compensation model under actual working conditions to obtain accurate external environment contact force. The application has the beneficial effects that all gravity compensation parameters can be calculated at high speed and effectively, and higher-precision and higher-efficiency gravity compensation is realized.

The invention provides a database index generation method and device and a data processing method and device, and relates to the technical field of computers, in particular to the technical field of distributed systems, storage management and the like. According to the specific implementation scheme, the method comprises the steps of determining to-be-aggregated items related to file quotas based on metadata of a metadatabase in the distributed file system; based on the to-be-aggregated items, determining an item aggregation function; creating an aggregation index in the metadatabase; performing statistics on the to-be-aggregated items according to the item aggregation function to obtain statistical data; and storing the statistical data in the aggregation index as aggregation data.

The application discloses a railway transportation enterprise non-traction energy consumption passenger and freight splitting method, which comprises the following steps: classifying railway transportation production units according to energy consumption types; selecting corresponding energy consumption passenger and freight splitting indexes from energy consumption splitting influencing factors for various energy consumption units; performing data cleaning on energy consumption data of each railway transportation enterprise, and classifying according to different energy consumption units based on different energy categories; determining energy consumption passenger and freight splitting data; constructing passenger non-traction energy consumption splitting models and freight non-traction energy consumption splitting models of various energy consumption units; and determining non-traction energy consumption passenger and freight splitting results of various energy consumption units. The application comprehensively considers influencing factors of railway transportation non-traction energy consumption passenger and freight splitting, and screens out main influencing factors as energy consumption splitting indexes, thereby improving the accuracy of non-traction energy consumption passenger and freight splitting, and effectively calculating non-traction energy consumption of passenger transportation and freight transportation of each energy consumption unit.

Embodiments of the present application provide a RefSPAD matrix opening method and related equipment for calculating valid RefSPAD matrix cell combinations. The method comprises: acquiring the number of photons received by each SPAD cell in the RefSPAD matrix; inputting the number of photons of all SPAD cells into a preset calculation model to determine whether there is a SPAD cell combination that meets a photon number threshold; and if so, determining that the SPAD cell combination is a SPAD that needs to be opened.

The application provides a kind of on-orbit multi-strip splicing calculation method and system, comprising: orbit recursion is carried out and is converted into position, velocity vector under satellite inertial system;Calculate the unit pointing vector of satellite to the specified center point vector under the orbit system;Adjacent strip center point distance is calculated;Calculate the azimuth of direction point, adjacent strip center point relative to the specified center point;Adjacent strip center point position, direction point position are calculated.The application solves the problem of multi-strip splicing calculation of high-resolution narrow-width wide observation satellite single-pass large-area imaging, can effectively calculate the latitude and longitude of the center point and azimuth point of each strip in multi-strip splicing, realize satellite single-pass multi-strip splicing large-scale imaging, and ensure the observation ability of satellite single-pass to large area.

The invention provides a safety distance monitoring method and system based on deep learning and perspective transformation, and relates to the technical field of substation safety monitoring. The method comprises the following steps: shooting transformer substation workers and marking shot images, and constructing a transformer substation worker foot key point detection data set; carrying out lightweight improvement on a YOLOv8 key point detection model, respectively adding a proposed DFF module and an ACC module to a backbone and a neck, and replacing an original PAN structure of a network with a BiFPN structure; performing perspective transformation on a substation scene based on a camera imaging principle to obtain distance information in the scene; and finally, based on the detected key points and the obtained mapping relation, completing the detection of the distance between the worker and the electrified equipment. According to the invention, an effective active monitoring means is established in a substation scene, so that early warning can be provided for workers, substation managers can be helped to take measures for timely intervention, and occurrence of electric shock accidents of the workers is effectively prevented.

The invention discloses a semantic segmentation model training method and an image detection method based on comparative learning, which are applied to the field of image detection, and comprise the following steps: extracting sample features of a training sample by using a feature extraction module; inputting the sample features into a semantic segmentation module and a comparative learning module to obtain a mask pattern and a feature pattern group; according to the label graph and the mask graph corresponding to the training sample, semantic segmentation loss is obtained through calculation; calculating comparison loss according to the feature data in the feature graph group and the feature queue; updating parameters of the semantic segmentation model according to the semantic segmentation loss and the comparison loss, and updating feature data in the feature queue according to the feature graph group and the feature data storage moment; obtaining a trained semantic segmentation model until a preset condition is met; the semantic segmentation model comprises a feature extraction module and a semantic segmentation module. According to the method, the occupation of memory resources can be reduced, the contrast loss of each iteration is effectively and accurately calculated, and the performance of the model is improved.