32results about How to "High computational complexity" patented technology

The invention belongs to the technical field of unmanned aerial vehicles, and particularly provides a transformer substation unmanned aerial vehicle autonomous inspection route generation method based on an optimization A star algorithm. According to the method, a heuristic function is improved, the Euclidean distance between a target point and a current node is comprehensively considered, obstacle avoidance processing is carried out, and a safer and more efficient inspection route is generated. And by calculating the total cost of each node, the node with the minimum total cost is selected for expansion until a target node is found. According to the improved A star algorithm, the unmanned aerial vehicle can be effectively prevented from flying into an obstacle dense area, the obstacle keeps a safe distance, and the flying safety is remarkably improved. The distance and the obstacle density are comprehensively considered, the generated inspection route is smoother and more efficient, and unnecessary flight distance and energy consumption are reduced.

The invention belongs to the technical field of atmospheric optics, and particularly relates to an atmospheric turbulence phase screen generation method based on complex variable matrix space-time interpolation. According to the invention, a complex variable matrix interpolation algorithm is used to simulate the continuous change of the light field intensity and the random phase screen in the propagation process; comprising the following steps: reflecting time continuity of a turbulent phase screen by using a random complex variable matrix according to a frozen flow hypothesis; based on a power spectrum inversion method, an atmospheric turbulence phase screen is generated; performing light field diffraction transmission simulation by using a Fresnel method; and displaying the change of results in different time steps by utilizing visual comparison. According to the invention, the transmission performance of the optical signal in a changing environment can be effectively improved, and the calculation complexity is greatly reduced.

The invention relates to the technical field of blurring detection, and discloses a motion blurring detection method and system based on deep learning and blind deconvolution, and the method comprises the steps: collecting an original image, and carrying out the sub-region division of the image; singular value decomposition is carried out on each sub-region; performing fuzzy region detection on the sub-regions according to a decomposition result; performing image restoration on the blurred image; and segmenting target contour information in the fuzzy restored image by using a deep learning method, and outputting a clear image with a target contour. By introducing the SPD-Conv, the improved YOLOv8 model can more accurately segment a low-resolution fuzzy target, and the segmentation precision is effectively improved. And the method can adapt to low-resolution fuzzy target segmentation tasks in different scenes. While the segmentation precision is improved, the calculation complexity and the reasoning time of the model are not remarkably increased, the real-time advantage of the YOLOv8 can still be kept, and the requirement for real-time performance in practical application is met.

The invention provides an economic dispatching strategy based on the charging space-time flexibility of an electric vehicle in a traffic-power distribution network, and the strategy comprises the steps: designing an economic dispatching total target, and building a three-layer architecture model; power distribution network data, electric vehicle detailed data, electric vehicle charging requirements and traffic network data of a plurality of charging stations are collected; aggregating electric vehicle charging requirements and establishing an electric vehicle charging flexibility model; establishing a first-layer scheduling model according to the economic scheduling total target, the electric vehicle charging flexibility model and the power distribution network data; establishing a second-layer scheduling model according to the economic scheduling total target and the detailed data of the electric vehicle; establishing a third-layer scheduling model according to the economic scheduling total target, the third-layer scheduling model and the traffic network data; and providing a charging scheduling scheme for the electric vehicle user based on the first-layer scheduling model, the second-layer scheduling model and the third-layer scheduling model. The impact of the charging load on the main power grid is reduced, and the total cost of an electric vehicle user is also reduced.

The invention discloses a method, a system, equipment and a medium for analyzing biological characteristic correlation in biomics data, and belongs to the technical field of bioinformatics. In particular to a method, a system, equipment and a medium for analyzing correlation of biomics data. According to the method, the system, the equipment and the medium, a high-performance and parallel block matrix algorithm is used for processing large-scale correlation and clustering tasks, excellent performance is shown in the aspect of calculating a correlation matrix, and downstream analysis can exceed computer memory resources; according to the method, the approximate P value can be quickly and effectively estimated without repeated complex cumulative distribution function calculation, and the corresponding approximate correction P value can be quickly and effectively estimated based on the P value sampling subset without being based on all the P values; and a top-k algorithm is realized, and faster execution and lower memory consumption are realized by utilizing a partial sorting method based on heaps.

The invention discloses an Android malicious application detection method based on a multi-view deep neural network, and relates to the technical field of information. The invention provides an Android malicious application detection method based on a multi-view deep neural network in view of the technical problems of numerous attack means, complex and changeable semantics, difficulty in ensuring the coverage rate of attack behaviors by executing detection only from a single view and the like of the current Android malicious application. According to the method, multi-view static feature information is organically fused, the synergistic effect of static features under all views is fully exerted, a target object is comprehensively described, limitation and fuzziness caused by application analysis from a single view are avoided, and finally accurate detection of malicious applications is achieved.

The invention discloses an electric power daily load prediction method based on policy quantification and a long-short-term memory algorithm, which belongs to the technical field of electric power system load prediction, and comprises the following steps: converting policies into quantitative indexes according to qualitative description of the policies, generating numerical characteristics reflecting the influence of policy changes on electric power demands, and calculating the electric power daily load prediction results; performing data preprocessing through abnormal value cleaning, missing value filling and normalization operation to obtain input data of the model; based on a long short-term memory network, model training is carried out according to input data, and a prediction model is generated by initializing model parameters, setting training hyper-parameters and a self-adaptive training strategy and adjusting dynamic parameters and is used for predicting the daily power load. According to the method, the power load change can be quickly and accurately predicted, so that the uncertainty caused by policy adjustment is effectively dealt with, and the operation efficiency and safety of a power system are improved.

This invention discloses a fall detection method, apparatus, device, and medium. The method includes: determining target noise point detection information corresponding to the target radar detection information of a target person based on personnel radar detection information detected by millimeter-wave radar and noise point detection information detected by an acoustic detection device; determining the detection mode of the target person based on the target radar detection information, and determining target detection information from the target radar detection information and target noise point detection information based on the detection mode; wherein the detection mode includes an obstruction mode, a stationary mode, and a motion mode; and detecting the fall state of the target person based on the target detection information. This invention uses a combination of millimeter-wave radar and acoustic detection for fall detection, enabling continuous fall state detection even when the target person is obstructed by objects indoors, improving the accuracy and efficiency of fall detection; and since both millimeter waves and acoustic waves are invisible waves, user privacy can be effectively protected.

The present invention provides a method of assessing the applicability of a fluorescent dye combination for generating flow cytometry data. A target method includes obtaining a fluorochrome combination, an instrument identifier, and a spectral matrix associated with the fluorochrome combination and the instrument identifier. The subject method further includes calculating an inverse matrix from the obtained spectral matrix, and identifying a fluorescent dye in the fluorescent dye combination associated with a flow cytometer data variance generated using the fluorescent dye combination by analyzing the calculated inverse matrix, the applicability of the fluorescent dye combination for generating flow cytometry data is evaluated. Meanwhile, the invention further provides a system for implementing the method and a non-transient computer readable storage medium.

The embodiment of the invention provides an audio restoration method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining lossless audio data and damaged audio data, carrying out the damage simulation processing of the lossless audio data, and obtaining simulated audio data, training a teacher network model based on the lossless audio data and the simulated audio data; inputting the damaged audio data into the trained teacher network model to obtain target audio data, and training the student network model based on the damaged audio data and the target audio data; and obtaining a to-be-processed audio signal, performing frequency domain transformation processing on the to-be-processed audio signal to obtain a frequency domain signal, inputting the frequency domain signal into the trained student network model to obtain a frequency domain repair signal, and performing time domain transformation processing on the frequency domain repair signal to obtain an audio repair signal. According to the scheme, the student network model deployed at the mobile terminal is obtained based on teacher network model training, and the real-time audio repairing requirement is met.

The invention belongs to the technical field of chip automation simulation, and particularly discloses a chip simulation post-processing method and system based on cloud computing, and the method comprises the steps: evaluating computing resources and storage resources needed by each post-processing task; based on the computing resources and the storage resources required by each post-processing task, allocating the computing resources and the storage resources to each post-processing task; and executing each post-processing task according to the parallel processing mode, and storing a processing result of each post-processing task. According to the method, the computing resources and the storage resources required by each post-processing task are evaluated, so that the cloud computing platform can dynamically expand or reduce the resources according to the data processing requirements, the computing resources and the storage resources are allocated to each post-processing task as required, and under the condition that enough resources are allocated to each post-processing task, the computing resources and the storage resources are allocated to each post-processing task. And the cloud computing platform can execute each post-processing task according to a parallel processing mode, and stores a processing result of each post-processing task.

The invention provides a polarization image defogging method based on a neural network, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of fogged polarization images, and generating a polarization image package; constructing a neural network defogging model; and based on the constructed neural network defogging model, carrying out defogging processing on the foggy polarization image in the generated polarization image packet to obtain a fogless image. According to the image defogging method, the deep learning and the polarization imaging technology are combined, the problem that the calculation complexity of the polarization imaging technology is high can be effectively solved, the overall generalization ability and defogging level are improved, and better robustness and high adaptability can be shown in the face of different types of fog and different degrees of haze.

The invention provides a multi-strategy improved distributed cross-domain cooperative task allocation optimization method, and belongs to the technical field of planning control. The method comprises the following steps: firstly, constructing a target allocation optimization model based on three types of target functions of comprehensive efficiency, time cost and resource utilization rate; a control distribution optimization model is constructed based on three objective functions of sensor detection capability, sending link connectivity and return link connectivity; and then combining the two models and corresponding constraint conditions to construct a cross-domain collaborative task allocation optimization model. And finally, carrying out distributed cross-domain collaborative task optimization allocation calculation by adopting a multi-strategy improved particle swarm optimization algorithm. According to the multi-strategy improved distributed cross-domain cooperative task allocation optimization method, the overall combat effectiveness is improved, the concealment and the collaboration among combat nodes are enhanced, powerful technical support is provided for cross-domain cooperative combat, and the task execution efficiency and the resource utilization benefit can be effectively improved.

The present invention provides a method for detecting insulator faults of transmission lines based on artificial intelligence, comprising the following steps: obtaining historical insulator images of several transmission lines through a drone, and marking the status of the insulators in each of the images; preprocessing the images and using them as training data sets; the preprocessing includes Gaussian blurring, Gaussian noise addition, and image sharpening; constructing a fault detection model based on YOLO, inputting the training data set into the model for training, and updating the hyperparameters of the model; inputting the transmission line insulator images to be detected into the trained fault detection model, and obtaining the corresponding insulator status detection results. The present invention detects insulator faults based on artificial intelligence technology, effectively solves the technical problem of many types of insulator defects in the prior art, realizes the automatic detection of insulator faults, and significantly improves the efficiency and accuracy of insulator fault detection.

The invention belongs to but is not limited to the technical field of artificial intelligence, and discloses an Alzheimer's disease diagnosis method based on electroencephalogram signal channel selection and a gating circulation unit, which selects an electroencephalogram channel through a particle swarm algorithm, reduces redundant information in electroencephalogram signal data, and improves the accuracy of electroencephalogram data analysis; feature extraction is carried out on the electroencephalogram signal data through principal component analysis, and the importance and distinction degree of features are improved; and finally, inputting the electroencephalogram signals into a gated circulation unit neural network for classification prediction, thereby reducing the number of electroencephalogram channels and reducing the dimension of data input. And the calculation efficiency is improved while the classification performance is ensured. According to the method, the classification performance of the Alzheimer's disease patients and the healthy subjects is improved, and accurate and efficient classification of the Alzheimer's disease patients and the healthy subjects is achieved.

The invention relates to the technical field of computers, in particular to a method for generating an image with scene characters. The method comprises the steps of obtaining text description; obtaining a background image, to-be-generated characters and a text area based on the text description; obtaining a local image based on the background image through an adaptive text block exploration strategy; obtaining a character image through the to-be-generated character; condition embedding is obtained based on the local image and the character image; establishing image guidance based on the text region; inputting the condition embedding and the image guidance into a convolutional neural network to obtain a decoded image; the character generation in the decoded image is determined by the character to be generated and the text area. The problem of small text generation during image generation is solved, the accuracy of text generation is improved, and the small text rendering effect during image generation is good.

The invention relates to an ROV simulator visual rendering display method and platform, and the method is characterized in that the method comprises the steps: obtaining an initialization resource, carrying out the modeling of a three-dimensional virtual model, and randomly placing a virtual camera in a virtual scene; receiving user interaction operation data; according to data acquired by the virtual camera and user operation data, drawing a three-dimensional virtual model through a four-stage mode of event response, data updating, model cutting and primitive drawing; and forming a display window by using three or more physical display screens, and receiving and displaying the drawn three-dimensional virtual model. Compared with the prior art, the mode that one graphic rendering computer is externally connected with three display screens to output 12 paths of virtual cameras is adopted, the input of hardware purchase, energy consumption and space arrangement is reduced, the visible cost is greatly reduced, a rendering program is compiled in a four-stage mode, and the rendering efficiency is improved. And the resource performance consumption between the CPU and the GPU of the graphic computer is balanced.

The application relates to the technical field of direction of arrival estimation, and specifically discloses a cross-platform complex domain feature enhancement coherent super-resolution DOA estimation method, which comprises the following steps: firstly, a complex domain CVSIMO learning model is constructed; sampling data is preprocessed; forward propagation of the CVSIMO learning model is carried out; backward propagation and parameter optimization of the CVSIMO learning model are carried out; data reconstruction and signal separation are carried out; cross-platform super-resolution DOA estimation is carried out; and finally, model simulation experiments are carried out. The cross-platform complex domain feature enhancement coherent super-resolution DOA estimation method can realize feature mining of multiple point source signals, converts a multi-source estimation problem into a single point source DOA estimation problem by using a complex domain neural network model, enhances the features of coherent signals by using the advantages of the complex domain neural network, improves the performance and precision of the super-resolution DOA estimation algorithm, can also realize data separation of multiple coherent signals and cross-platform DOA estimation, and realizes real angle estimation through ingenious feature solving.

The present invention relates to a radio wave propagation channel modeling method for predicting received signal power, comprising the following steps: S1, inputting a digital map of a target measurement area; S2, determining an ellipsoidal Fresnel zone, a first spherical zone, and a second spherical zone according to the position of a transceiver; S3, setting a scatterer position and the probability that the scatterer can affect radio wave propagation according to the area determined in step S2, and updating the scatterer distribution according to the movement of the transceiver; S4, simulating radio wave propagation using a hybrid simulation model of ray tracing and propagation graph theory; S5, training a statistical model of line-of-sight propagation path fading under set obstruction according to measurement data, embedding it into a simulation model, and obtaining an optimized prediction model; S6, outputting a predicted reception level and channel characteristic parameters. Compared with the prior art, the present invention simulates radio wave propagation in a line-of-sight path with certain obstructions in an urban scene, and has the advantages of low algorithm complexity and high prediction accuracy.

The present invention belongs to the technical field of human behavior and action recognition, and specifically relates to a method and system for rapid human behavior recognition based on a multidimensional skeletal spatiotemporal relationship graph network. The method comprises: constructing a teacher network model, using a multidimensional skeletal spatiotemporal relationship graph network MM-STGCN to construct the teacher network model, MM-STGCN comprises a plurality of basic blocks, each of which comprises two modeling types: multidimensional adaptive spatial topology dynamic modeling MDAST and multidimensional temporal feature modeling MLTF; MDAST: constructing a special multidimensional adaptive spatial topology graph to extract spatial features from different levels and enhance semantic feature representation; constructing a recognition model, providing additional supervised training to the student network model based on a knowledge distillation strategy through the loss function provided by the teacher network model; using a recognition function to calculate the distillation loss of the student network model; and using the trained student network model as a recognition model for rapid human behavior recognition.

A method and system for scheduling space-time resources of an aircraft cluster, the method comprising: establishing a space resource scheduling mathematical model corresponding to each time slot type according to node scheduling rules corresponding to different time slot types in a network to be planned, the mathematical model comprising a node set and a node-transceiving pair set; running a singularity arrangement algorithm on the mathematical model to group all transceiving pairs that do not conflict with each other in a time slot into a node-transceiving group; determining the service weight of each node-transceiving group in a current communication period according to the service weight of each node in a previous communication period, and sorting the node-transceiving groups according to the service weight, and allocating time slots to all node-transceiving groups in turn according to the sorting. The application significantly reduces the resource scheduling calculation complexity by respectively implementing space resource scheduling and time resource scheduling, while ensuring high throughput, achieving high scheduling efficiency and flexible adaptation to the service time-varying nature.

The invention relates to the technical field of microscope imaging, in particular to a microscopic imaging method and device based on grating illumination, equipment and a medium. According to the application, stripe-shaped illumination light is formed through the grating device with alternately arranged light-transmitting areas and light-proof areas, the imaging camera is automatically triggered to capture the sample image after each movement of the grating device, the background picture and the highlight picture are created, and the gray value difference of corresponding pixel points of any two captured images is calculated, so that the image quality is improved. And when the gray value difference is smaller than or equal to the preset threshold value, the corresponding pixel values in the background picture and the highlight picture are updated, so that the imaging time is greatly shortened, the imaging efficiency is improved, the complex calculation processing is reduced, and the calculation complexity is reduced.

The application discloses a load aggregation body prediction method and system of an integrated framework, and the method comprises the following steps: applying a prediction result of a trained load aggregation body prediction model to perform scheduling; wherein the construction of the load aggregation body prediction model comprises the following steps: obtaining load aggregation body historical data; performing external influence factor analysis on the load aggregation body historical data, and screening important external influence factors; applying a K-shape algorithm to cluster the load aggregation body historical data, and dividing the load aggregation body historical data into a plurality of clusters; constructing a load aggregation body prediction model, including a CNN-LSTM-ATTENTION prediction model and a GBDT prediction model; performing load prediction on each cluster through the CNN-LSTM-ATTENTION prediction model by using the screened important external influence factors and the divided plurality of clusters; and applying the GBDT prediction model to perform integrated prediction on the load prediction results of the clusters, thereby obtaining a load aggregation body prediction result; and the application can improve the prediction accuracy of the load aggregation body prediction model.

The application discloses a general information transmission optimization method, comprising the following steps: setting the number of satellite communication sites as N, and the time interval of message transmission between the satellite communication sites as 1 minute; making the N satellite communication sites perform forward transmission in the 0th minute to the (N / 2)-1th or ((N-1) / 2)-1th minute of the message transmission; making the N satellite communication sites perform reverse reverse sequence transmission in the (N / 2)-1th or ((N-1) / 2)-1th minute to the (N / 2)+[(N / 2)-1]th or ((N-1) / 2)+[(N-1) / 2)-1]th minute of the message transmission; obtaining a signal transmission model under the cyclic recursive transmission of the forward information transmission and the reverse reverse sequence information transmission, and optimizing the information transmission based on the signal transmission model. The application has high realization, is easy to understand, can simplify complex problems, and can be realized through programming, so that the calculation complexity is greatly improved.

The present invention belongs to the technical field of wireless communication networks, and discloses a wireless sensor network routing recovery method, device and medium to solve the problem of how to quickly select alternative solutions to maintain the connectivity of the network when a fault occurs in the wireless sensor network. Based on the concept of suboptimal spanning trees, a set of suboptimal spanning tree solution sets can be generated by inputting the topological structure of the network. These suboptimal spanning trees can be used as alternative solutions, and alternative solutions can be quickly selected to maintain the connectivity of the network when a fault occurs. When a fault occurs, the suboptimal solution can be used as an alternative solution in time to maintain the connectivity of the network.

The application provides a single-pixel imaging multi-target positioning method based on multi-direction projection optimization, and relates to the field of moving target positioning and tracking, which comprises the following steps: based on prior position information of multiple targets, a Hough transform is used to extract a candidate projection angle set of collinear targets, and clustering analysis is performed on multiple candidate projection angles to obtain a key projection angle set; by using the key projection angle set, a Fourier central slice theorem is used to obtain one-dimensional projection curves corresponding to each key projection angle in the corresponding direction in the target scene; according to the one-dimensional projection curves, projection strips of the targets in the corresponding direction are extracted, combined strips are generated through spatial mapping and splicing, and an image containing a target region is obtained; after morphological filtering processing is performed on the image, position estimation is performed by using a centroid coordinate, and the position positioning of multiple targets is obtained. The application can effectively reduce the projection angle, reduce the measurement times and improve the positioning efficiency under the premise of ensuring the positioning accuracy.

The invention discloses a broadband radiation source direct positioning algorithm based on data fusion and frequency focusing, and the algorithm comprises the steps: carrying out the J-point FFT of data received by each observation station; then, under each frequency point, carrying out fusion processing on the received data of all the observation stations to obtain J groups of received data; then, covariance matrixes of the J groups of data are calculated, eigenvalue decomposition is carried out on the covariance matrixes, and signal subspaces are separated out; then, calculating a focusing matrix corresponding to each frequency point to obtain a final covariance matrix; then, carrying out eigenvalue decomposition on the final covariance matrix, and uploading a noise subspace to a central station; thirdly, gridding the positioning area at the central station, and calculating the function value of the position estimation function in each grid coordinate; and finally, searching a plurality of grid coordinates of the radiation source with the maximum function value, and taking the position as a positioning result. Compared with the existing algorithm, the method provided by the invention has higher positioning precision in a direct positioning scene of the broadband radiation source.

The invention discloses a calculation method for electromagnetic wave reflection propagation in a U-shaped roadway. The calculation method comprises the following steps: S1, constructing an electromagnetic reflection model of the U-shaped mine roadway by adopting a mirror image method; s2, according to the electromagnetic reflection model of the U-shaped mine laneway, effective reflection points are obtained through a mirror image method; s3, obtaining an effective reflection path through a mirror image method according to the effective reflection points; and S4, according to the effective reflection path, obtaining reflection field intensity distribution of each point in the path. By adopting the technical scheme of the invention, the problem that the electromagnetic wave conduction calculation becomes more complicated due to the influence of multiple reflection and diffraction on the electromagnetic wave in the U-shaped mine tunnel is solved.

The application discloses a decision tree binning calculation optimization method, equipment, medium and computer program product. The decision tree binning calculation optimization method comprises the following steps: obtaining memory storage data corresponding to a target decision tree and an index table corresponding to the memory storage data, wherein the index table at least comprises a node binning label corresponding to each decision tree binning; then, based on the size of each decision tree binning, the node binning label is segmented to obtain a segmented batch of each binning label; then, based on the index table, each decision tree binning batch data is extracted; then, based on the decision tree binning size information corresponding to each segmented batch of binning label, a corresponding number of parallel computing threads is matched for each decision tree binning batch data; then, based on the number of parallel computing threads, parallel decision tree binning calculation is performed on each decision tree binning batch data to obtain a target decision tree binning calculation result. The application solves the technical problem of low calculation efficiency during decision tree binning.

This invention provides a Fourier-Melin transform image geometric matching method and system with decoupled logarithmic domain, comprising: estimating the rotation angle using Fourier-Melin transform; rotating the image to be matched in the current frame relative to the template image in the previous frame to obtain an axially aligned image search region; transforming the template image and the image to be matched from Cartesian coordinates to logarithmic polar coordinates to obtain the image search region corresponding to the logarithmic polar coordinates; sequentially segmenting the image search regions corresponding to the image to be matched and the template image along the θ-axis according to a set ratio; stitching the horizontally correlated and vertically correlated image regions into two twin image search regions; calculating the cross power spectrum through phase correlation to obtain the scale factors in the horizontal and vertical directions; and aligning the images to be matched according to the scale factors in the two directions. This invention has the ability to estimate scale and rotation proportionally, and is scale adaptive, and is robust to out-of-plane flipping and rotation of the target object.