24results about How to "Increased propulsion speed" patented technology

The invention relates to a fluid trajectory tracking method based on a physical heuristic neural network, and the method specifically comprises the steps: building a first full-connection network, and obtaining a particle trajectory through the prediction of the first full-connection network; a second full-connection network is established, and the flow field speed and the flow field pressure are obtained through prediction of the second full-connection network based on the particle trajectory; connecting the first full-connection network and the second full-connection network in series, and training to obtain a prediction network; and performing fluid trajectory tracking through the prediction network. According to the method, the accuracy and efficiency of prediction are improved while the physical consistency is ensured.

The present application provides a point cloud reconstruction model generation method, a point cloud reconstruction method and an electronic device, and relates to the field of point cloud technology. The method includes: determining at least one target reconstructed ground in the ground to be reconstructed; performing image acquisition on each target reconstructed ground from different perspectives to obtain image pairs corresponding to each target reconstructed ground; determining the local optimization target corresponding to each target reconstructed ground based on at least one set of associated feature items in the image pairs corresponding to each target reconstructed ground; optimizing the basic point cloud reconstruction model corresponding to the ground to be reconstructed according to the local optimization target corresponding to each target reconstructed ground, and obtaining the target point cloud reconstruction model of the ground to be reconstructed. The present application achieves the effect of fast and accurate point cloud reconstruction of a large range of map elements.

The invention discloses a multi-unmanned aerial vehicle cooperative search method based on dynamic region division and a multi-constraint virtual force field, and aims to solve the problems of low search efficiency, unbalanced load, difficulty in guaranteeing the motion safety of multiple unmanned aerial vehicles and the like in multi-unmanned aerial vehicle coverage search. Firstly, a task area, an unmanned aerial vehicle kinematics model and a sensor sensing model are constructed; then, dynamic region division is carried out based on a weighted Voronoi diagram, the search progress of the unmanned aerial vehicle, the responsibility region area and the obstacle distribution condition are comprehensively considered, and self-adaptive balancing of task loads is achieved; and selecting a target point based on a utility function, and finally designing a multi-constraint virtual force field control method consisting of target attraction, obstacle avoidance force, regional barrier force and exploration force, generating an expected speed meeting speed constraint and obstacle avoidance requirements, and realizing stable and efficient coverage search. The result shows that the multi-machine collaborative search efficiency can be effectively improved, and the method is suitable for disaster search, routing inspection monitoring, autonomous detection in a complex environment and other scenes.

The invention provides a computing task unloading optimization method considering multiple performance indexes, and belongs to the technical field of edge computing, the method adopts an orthogonal multiple access technology and a non-orthogonal multiple access technology in a mixed mode to provide services for multiple users at the same time, and the method comprises the steps that the time cost of each computing task under a preset computing path is calculated; inputting the time cost of each calculation task into a pre-established calculation task allocation model to generate a preliminary allocation scheme; establishing an MINLP model of a calculation task unloading optimization problem; and solving the MINLP model by adopting a Lagrange relaxation method and a dual decomposition method by taking the preliminary allocation scheme as a starting point, and obtaining an optimal calculation task unloading optimization scheme. According to the method, when the MINLP model is solved by using the Lagrange relaxation and dual decomposition method, the calculation task allocation model is adopted to provide a preliminary allocation scheme, so that the search range is reduced, the calculation complexity is reduced, and the real-time performance of the system is improved.

The application provides a kind of deep learning optical imaging system design method based on complex function neural network, comprising the following steps: step one, object distance, system focal length, F number as input, according to the ideal optical system imaging relationship, determine image distance, imaging scale ratio;Step two, according to the imaging scale ratio and the object image of ideal optical system, the image of ideal optical system is calculated;Step three, construct complex function generator network model, step four, construct discriminator network model, step five, after the establishment, training of the above-mentioned complex function neural network deep learning model, step six, use phase.The deep learning optical imaging system design method based on complex function neural network provided by the application solves the correlation between the advantages and disadvantages of the design result and the experience value of the designer, without the designer providing initial structure parameters;Greatly improve the computing ability and optimization speed.

The invention discloses a drill rod grabbing and placing method, device and equipment and a storage medium, and the method comprises the steps that in response to a placing instruction, a target placing finger board gap is determined; a manipulator is controlled to grab a to-be-placed target drill rod and move the to-be-placed target drill rod to a waiting position before the to-be-placed target drill rod extends into a gap of The number of existing drill rods in the target finger board placing gap is obtained, and the target length is obtained according to the number of the existing drill rods; according to the target length, a target rotation angle needing to rotate of a center shaft of the manipulator is obtained; according to the preset initial speed of the manipulator, the manipulator is controlled to push the to-be-placed target drill rod from the waiting position to the buffering position; and under the condition that the to-be-placed target drill rod reaches the buffer position, the manipulator is controlled to execute a buffer pushing strategy until the manipulator pushes the to-be-placed target drill rod from the buffer position to the target placement position according to the target rotation angle. According to the drill rod placing device, the impact of the drill rods reaching the tightly-arranged positions in the drill rod placing process can be reduced.

The present application discloses a tetrahedral mesh generation method and related devices, which relate to the field of mesh generation, including: performing Delaunay triangulation based on an initial point set to obtain an initial tetrahedral mesh, inserting each triangular face contained in a target triangular face model into the initial tetrahedral mesh to obtain a first updated tetrahedral mesh, optimizing the first updated tetrahedral mesh using a genetic algorithm to obtain a second updated tetrahedral mesh, and when a preset iteration termination condition is not reached, using all grid points of the second updated tetrahedral mesh as an initial point set, returning to perform Delaunay triangulation based on the initial point set, until a preset iteration termination condition is reached, extracting the tetrahedral mesh belonging to the target triangular face model in the second updated tetrahedral mesh to obtain a target tetrahedral mesh. The present application adopts a combination of a genetic algorithm and volume extraction to obtain a target tetrahedral mesh with better quality, thereby improving the optimization speed and efficiency.

The invention discloses a transformer substation site selection data processing and feedback method, system and device based on unmanned aerial vehicle-AI cooperation and a medium, and belongs to the technical field of intelligent site selection of electric power engineering, and the method comprises the steps: controlling an unmanned aerial vehicle to shoot a transformer substation site selection region according to a preset flight route, and obtaining a site selection region image; performing preprocessing and feature extraction on the site selection area image to obtain key environment element information; inputting the key environment element information into a pre-trained site selection environment evaluation model to obtain an evaluation result, including a terrain suitability grade, infrastructure proximity and a comprehensive site selection feasibility score; according to the evaluation result and a preset engineering constraint rule base, matching an avoidance suggestion; and displaying the avoidance suggestions. According to the method, site selection suggestions can be provided for decision makers in a shorter time, the advancing speed of substation construction projects is increased, the advantages and disadvantages of site selection can be judged more objectively and accurately, and the risk of site selection errors caused by human factors or insufficient data is reduced.

The present invention provides a method for optimizing fracture parameters based on Gaussian process regression, comprising the following steps: obtaining a set of daily production data and cumulative production data under different fracture parameters through simulation as a basic data set, and establishing an implicit function of fracture parameters, daily production and cumulative production in the basic data set; dividing the basic data set into a training set and a test set, and respectively obtaining a Gaussian process regression daily production model and a Gaussian process regression cumulative production model; establishing an objective function according to the two Gaussian process regression models; setting an initial population, selecting better individuals in the initial population, and performing iterative calculations on them using a genetic algorithm to select the optimal fracture parameters; the present invention can effectively optimize the fracture parameters of a tight gas fracturing well, including the optimal fracture half-fracture length, the optimal fracture seam spacing, and the optimal fracture permeability, and can also greatly improve the calculation efficiency and realize real-time optimization.

The utility model relates to photovoltaic panel device technical field, and disclose a kind of quick installation type photovoltaic panel device, including mounting seat, four rectangular equidistant distribution slide rail box are fixedly connected in mounting seat outer side wall, the inner wall one end of slide rail box is slidably connected with sliding arm, the one end output of sliding arm extends to the inner wall one end of slide rail box, the top of mounting seat is fixedly connected with two equidistant distribution spring seat, the inner wall bottom of spring seat is fixedly connected with two springs, the top of spring is fixedly connected with pressboard, and the device is realized by the cooperation of above-mentioned structure, the quick, accurate installation of photovoltaic panel is realized, installation efficiency is effectively improved, installation accuracy and stability are guaranteed, the frequency of later maintenance and replacement is reduced, maintenance cost is reduced, with significant economic benefits and application value.

The present invention relates to a tunnel lining structure internal hollow disease detection equipment and detection method based on a laser shock method, using a high-power short-pulse high-energy pulse laser transmitter to generate a laser shock wave to generate an instantaneous force on the tunnel lining surface, and then a laser Doppler vibrometer measures the vibration generated on the tunnel lining surface after the force is applied and records the vibration frequency, and through time-frequency domain conversion, removes various noise frequencies mixed in the result, analyzes the subtle changes in the vibration frequency, and once a hollow disease is found, immediately records the mileage of the disease and alarms, completing the rapid detection of the hollow inside the tunnel lining. Laser shock wave technology has the advantages of small heat-affected area, high pressure, superposition, non-contact, easy to accurately locate and control, etc.

The invention discloses a probe coupling control parameter optimization method based on a particle swarm optimization algorithm, and aims to improve the coupling effect and detection precision of a probe in high-speed wheel detection. In the wheel set detection process of a high-speed train, an ultrasonic probe is influenced by wheel surface irregularity and high-speed movement and is difficult to keep a stable coupling state, so that the detection precision is reduced. In order to solve the problem, a particle swarm optimization (PSO) algorithm is adopted to automatically optimize key parameters of the probe, including probe quality, spring stiffness coefficient, probe extension length and the like. The method has the advantages of fast parameter optimization process, high global convergence capability and the like, can effectively improve the coupling stability and detection precision of the ultrasonic probe, and is suitable for the field of automatic flaw detection of high-speed railway wheel sets.

The present invention relates to a laser-driven multi-shell fusion target design method and device based on hybrid optimization. The method comprises: obtaining an initial neutron yield; generating a first input parameter by normalizing a random variable; performing numerical simulation calculation according to the first input parameter to obtain a first neutron yield; comparing the first neutron yield with the initial neutron yield to perform cyclic convergence control until the random walk step length is less than or equal to the random walk step length accuracy, and obtaining multiple sets of parameters in the optimization process; constructing an initial input-output model using multiple sets of parameters as initial data; solving a second input parameter, and calculating the corresponding second neutron yield; iteratively optimizing the initial input-output model through the second input parameter and the second neutron yield to obtain an optimized input-output model; and estimating the optimal parameters based on the optimized input-output model. The present invention does not require manual adjustment of input parameters, accelerates optimization through cyclic convergence control, has high efficiency, and has low time cost.

The present invention discloses a method for improving the efficiency of collaborative optimization of light sources and mask plates, comprising the following steps: step S1, generating an OPC initial optical model and generating a simulation calculation graph; step S2, setting various parameters according to the SMO process, and setting n different normalized energy doses and m different focal length values; step S3, running n×m SMO optimization processes at the same time; obtaining process records of n×m SMO tasks and preliminary results of SMO; step S4, comparing the similarity of n×m SMO lighting source shapes, and comparing evaluation parameters and time; step S5, determining the most optimized energy dose and setting the focal length value, and performing SMO optimization. The present invention improves the SMO optimization efficiency, speeds up the SMO optimization speed, and also lays a solid foundation for SMO to output a suitable lighting source shape.

The invention belongs to the field of steel-concrete connection section design, and discloses a steel-UHPC (Ultra High Performance Concrete) geocell shear capacity test and determination method, and all shear connectors (studs or perforated steel plates (PBLs)) of a steel-UHPC geocell longitudinal shear transfer capacity push-out test piece are located in a geocell. The invention accurately reflects a reasonable form of a steel-UHPC cell longitudinal shear transmission performance and a bearing capacity push-out test piece, and a method for determining the bearing capacity of PBL steel-UHPC cell longitudinal shear transmission.

The present application discloses a method, device, equipment and storage medium for grabbing and placing drill rods, the method comprising: determining the target placement finger beam gap in response to a placement instruction; controlling a manipulator to grab the target drill rod to be placed and move it to a waiting position before extending into the target placement finger beam gap; obtaining the number of existing drill rods in the target placement finger beam gap, and obtaining the target length according to the number of existing drill rods; obtaining the target rotation angle that the central axis of the manipulator needs to rotate according to the target length; controlling the manipulator to push the target drill rod to be placed from the waiting position to the buffer position according to the preset initial speed of the manipulator; when the target drill rod to be placed reaches the buffer position, controlling the manipulator to execute a buffer push strategy until the manipulator pushes the target drill rod to be placed from the buffer position to the target placement position according to the target rotation angle. The present application can reduce the impact of the drill rods reaching a tightly arranged position during the placement of the drill rods.

This invention provides a method for accelerating real-time inference of multimodal large models, relating to the field of data processing technology. The method includes: Step 1, generating a minimum envelope ellipse using the peak point of the first visual attention weight, the second visual coordinate point, and the third visual association point as boundary points, and calculating the ratio of the area to the perimeter of the ellipse as a spatial clustering index. This invention effectively improves the efficiency, accuracy, and resource utilization of real-time inference of multimodal large models by selectively enhancing spatial features, evaluating multidimensional constraint computing power and dynamically scheduling resources, lightweight cross-modal fusion, and multi-step inference attention calibration, combined with real-time decision-making and full-process data-driven evaluation parameters and dynamic updates of the model library. This ensures the real-time performance and reliability of inference results, while continuously optimizing system computing power evaluation and model adaptation, making multimodal inference more aligned with the resource constraints of heterogeneous computing and the real-time and accuracy requirements of actual business operations.

The present application belongs to the technical field of railcar management, and specifically relates to a method and system for guiding and assisting before shunting operation of a freight train, which comprises collecting real-time front approach environment data through a multi-modal sensing module deployed at the front end of the shunting operation during the shunting and pushing operation of the freight train; and transmitting the front approach environment data to an edge computing node; the present application realizes accurate matching of visible light images and radar point cloud data through multi-modal fusion collection of a three-lens high-definition camera unit and a millimeter wave radar, combined with millisecond-level space-time synchronization technology, effectively making up for the visual blind area of traditional manual lookout in adverse weather such as rain, snow and fog, and at night and in complex station environment. Among them, the accurate measurement of obstacle distance and relative speed by the millimeter wave radar, combined with image visual feature capture, expands the environmental perception coverage range, reduces the obstacle identification missed detection rate, and completely solves the core pain point that the accuracy of manual judgment is greatly affected by the environment.

The invention discloses a collaborative clustering-based cross-subject speech imagination electroencephalogram intention decoding domain migration method, which comprises the following steps of: 1, acquiring existing speech imagination electroencephalogram data so as to construct a source domain data set and pre-process the source domain data set; 2, extracting speech imagination-related electroencephalogram features in the preprocessed source domain data set; 3, initializing a clustering indication matrix of the samples and a clustering indication matrix of the features, and solving and optimizing; 4, training a semi-supervised speech imagination intention decoding model based on electroencephalogram samples and feature collaborative clustering; step 5, collecting target domain data, and performing preprocessing; step 6, determining optimal source domain matching of the target domain; step 7, obtaining an adjusted coefficient matrix; and step 8, obtaining a final optimized sample feature coupling clustering electroencephalogram speech imagination intention decoding matrix decomposition model on the target domain, and then converting an identification tag of intention decoding into a clustering indication matrix of a target domain sample and solving the clustering indication matrix.

The invention discloses a method and a device for judging concave and convex angles of a house type image based on a region growing algorithm. The method comprises the following steps of: performing reconstruction filling on an outer contour mask image by utilizing Hough straight line detection; calculating a maximum inscribed rectangle coordinate of the reconstructed mask, and extracting horizontal and vertical straight line information of the reconstructed mask by utilizing Hough straight line detection; taking the current maximum inscribed rectangle as an initial main body area and an initial detection area, scanning a discrimination block between the periphery of the detection area and the nearest straight line anticlockwise circularly from the position right above the detection area, and if the discrimination block meets the convex angle condition, recording the coordinate of the area and expanding the detection area; and if the judgment block meets the concave angle condition, recording supplementary angle coordinates of the judgment block and expanding the main body area and the detection area until no nearest straight line exists around the detection area. According to the method, based on the region growing algorithm, the periphery is scanned from the current region to search for the judgment blocks meeting the conditions, the main body is expanded, and the concave-convex angles of the house type image can be accurately and rapidly extracted.

The invention discloses a control method and system of a vertical shaft heading machine, and is applied to the field of vertical shaft heading machines, the method comprises the following steps: controlling the vertical shaft heading machine to adjust the working state thereof by using a received control signal, the control signal generation process comprises the following steps: obtaining geographical environment change information, and determining the working state of the vertical shaft heading machine based on the geographical environment change information; determining the stratum sudden change intensity of the working area; determining an energy consumption disturbance index at least based on the stratum sudden change intensity; processing the energy consumption disturbance index to obtain a predicted energy consumption change rate of the working area; determining energy consumption prediction information based on the predicted energy consumption change rate and the corresponding stratum sudden change intensity; acquiring electrical sampling data; verifying initial risk probability data determined based on the electrical sampling data to generate fault risk information; and generating a control signal based on the energy consumption prediction information and the fault risk information. According to the control method and system of the vertical shaft heading machine, the parameter adjustment accuracy is remarkably improved through accurate analysis of data.

This invention discloses an efficiency optimization control method for a high-speed brushless DC motor system based on time-sharing commutation. It establishes a time-sharing commutation mode to determine the variation law of the motor phase current; obtains the turn-on offset angle and turn-off offset angle based on the maximum electromagnetic power using offline numerical solutions; designs the input layer, hidden layer, and output layer structure of an RBF neural network; performs offline pre-training of the RBF neural network using prior information and online training; updates the neural network parameters by acquiring real-time motor system efficiency information; and applies the optimal turn-on offset angle and optimal turn-off offset angle output by the neural network to the time-sharing commutation control, thereby improving the motor's operating efficiency. The advantages of this invention are that it is not limited to the 120° conduction mode of the brushless DC motor windings, but establishes a time-sharing commutation mode; and it utilizes the RBF neural network to quickly optimize the turn-on offset angle and turn-off offset angle, thereby improving the motor system efficiency.

The application provides a two-dimensional separated sparse array, and relates to the technical field of antenna engineering. The two-dimensional separated sparse array is arranged horizontally and vertically, and array arrangement is neat; the sub-arrays in four corners and the sub-arrays in the middle cross region can be integrated according to rows or columns, which is convenient for the design of the rear-end feed network and the modular installation; the two-dimensional separated sparse array is formed by nesting a first basic sparse array and a second basic sparse array; the first basic sparse array and the second basic sparse array can be decomposed into sparse linear arrays for optimization; the number of optimization variables is reduced from 2MxN to 2(M+N) after decomposition, and only the directional diagram of a section needs to be calculated during optimization, which greatly reduces the number of optimization variables and the calculation amount, and greatly improves the optimization speed; the sidelobes of the two basic sparse arrays have a certain cancellation effect, which can further reduce the maximum sidelobe level of the overall sparse array, and has the characteristics of low three-dimensional space sidelobe level.

The invention relates to an irradiation plan optimization method based on novel sparse sampling, belongs to the technical field of radiation irradiation dose optimization, and solves the problems that invalid voxel data volume cannot be effectively simplified, the processing data volume is large and the result is not accurate enough in the prior art. Receiving an initial irradiation plan and generating a full dose deposition matrix corresponding to each radiation field; constructing a gradient image based on the full dose deposition matrix; performing dual-condition voxel screening on the constructed gradient image to obtain a final sampling voxel set, and constructing a sparse dose deposition matrix; and performing irradiation plan optimization and dose normalization on the sparse dose deposition matrix to obtain an optimized irradiation plan.