190 results about "Local space" patented technology

The invention discloses an automatic welding device for a rotary alloy furnace tube, and belongs to the technical field of arc welding equipment.The automatic welding device for the rotary alloy furnace tube comprises a base and a U-shaped bin located at the top of the base, and the two ends of the interior of the U-shaped bin are jointly provided with a centering clamping mechanism used for clamping the outer side of the furnace tube; a driving mechanism for providing power for the centering clamping mechanism to clamp the furnace tube is arranged on the outer side of the centering clamping mechanism, and a welding mechanism is arranged on the inner top of the U-shaped bin. A local sealing design is adopted for the inflation mechanism, the annular air bag is attached to the inner wall of the furnace tube after being inflated, an independent cavity communicated with the outside only through a welding seam gap is formed near the welding seam, the protection requirement can be met only by filling the local space with argon, the whole furnace tube or a large-range external space does not need to be filled, and compared with traditional overall argon filling, the argon filling efficiency is greatly improved. The argon consumption is greatly reduced, the industrial gas purchase cost is remarkably reduced, and the production cost investment is reduced.

The invention discloses a dynamic inspection method and device based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the technical field of software and platforms or other related technical fields, and the method comprises the steps: carrying out the three-dimensional scene modeling of a to-be-inspected region through the space-time alignment data obtained through preprocessing; acquiring an inspection operation instruction, and performing inspection task distribution of multiple flight constraints on the dynamic environment model obtained by modeling to obtain an optimized task execution scheme; and performing global path planning and local space-time cooperative path optimization on the task execution scheme based on the high-precision three-dimensional scene model obtained by modeling to obtain a target inspection path, thereby remotely controlling the target unmanned aerial vehicle to perform optimized path inspection and anomaly analysis according to the target inspection path, and generating an anomaly inspection report of the target inspection area. According to the invention, the technical problems of poor adaptability and low accuracy of the inspection result of the inspection mode of planning the inspection path based on historical data in the prior art are solved.

The invention belongs to the technical field of computer vision, deep learning and time-space prediction, and discloses an end-to-end time-space prediction method based on improved three-dimensional rotation position coding, which is suitable for various time-space sequence prediction scenes such as weather, traffic flow and the like. According to the invention, through four key improvements, a position coding mechanism is optimized; three-dimensional coding proportions of time, height and width are dynamically adjusted so as to adapt to different scenes; fusing the absolute time and the relative space position, and strengthening local space-time correlation modeling; the position information directly guides attention calculation, and the fusion with an Attention module is deepened; and a rotation matrix cache mechanism is introduced to reduce redundant calculation. Meanwhile, the model is matched with a Patch embedding layer, an adaptive Transform encoder and an MLP de-wharf, a complete link of'feature embedding-position encoding-space-time fusion-prediction output 'is constructed, and the precision, generalization and reasoning efficiency of space-time prediction are effectively improved.

The invention provides a laser, inertia and image recognition combined calibration method and system. Comprising the following steps: setting a parameter calibration checkerboard on a marble platform for detection, and leveling the parameter calibration checkerboard; carrying out inertial navigation IMU static parameter calibration and inertial navigation IMU dynamic parameter calibration on the inertial navigation IMU, and correcting an inertial navigation IMU error; irradiating laser spots of a laser into the parameter calibration checkerboard, shooting the parameter calibration checkerboard from different angles through a machine vision camera, and obtaining a parameter calibration checkerboard photo set; and joint calibration of parameters of a machine vision camera, an inertial navigation IMU and a laser is carried out, and automatic correction of external parameters of various precise positioning measuring instruments and high-precision measurement of local space are realized.

The invention discloses an industrial multi-source heterogeneous data feature fusion and dynamic modeling method and system, and relates to the technical field of industrial multi-source heterogeneous data processing, and the system comprises a data collection module, an event triggering type local space-time alignment module, a time sequence data set generation module, a feature fusion module and an industrial equipment state model generation module. According to the method, an event-triggered local space-time alignment module is arranged, an event-driven dynamic space-time anchoring mechanism is adopted, and in a preset tolerant time window, an image feature time sequence is constructed through industrial camera superframe sampling to be matched with sensor time sequence data, so that feature dislocation caused by sampling frequency difference is avoided; compared with an existing interpolation method, the non-linear coupling relation between the process parameter data and the sensor time sequence data is obtained, and the problem of feature fusion distortion caused by the time granularity difference is solved by performing non-linear interpolation on the process parameter data through Gaussian process regression and generating a continuous proxy curve synchronous with the sensor time sequence data.

The invention provides a 66kV main transformer load prediction method based on self-coding and multi-model fusion, and relates to the technical field of load prediction. The method comprises the following steps: adaptively determining an optimal cluster number by using an elbow rule, and reasonably grouping load data; by constructing a self-encoding network based on data correlation, compression and feature reconstruction are performed on data in each cluster. The invention provides a load prediction method fusing a BiLSTM (Bidirectional Long Short Term Memory) model, an Informer model and an improved STSGCN model. The BiLSTM is used for capturing local time sequence features of data, the Informer processes long sequence data to mine long-term dependency and global information in the data, and the improved STSGCN module is used for capturing local space-time correlation of the data. And splicing the output features of the three models, adaptively adjusting the contribution of each module through a gating unit so as to carry out feature fusion, and finally carrying out output prediction through a full connection layer.

The invention provides a trajectory clustering method based on space-time weighting and a density peak value, and relates to the technical field of trajectory clustering. The method provided by the invention comprises the following steps: based on a minimum description length criterion, fusing an adaptive space-time weight and a space-time geometric distance to segment trajectory data to obtain sub-trajectory segments; calculating a space-time local density, a relative distance and a decision value of each sub-track segment based on a density peak clustering algorithm; performing noise identification on the sub-track segments based on the average space-time geometric distance and the average local space-time density, iteratively selecting a class cluster center based on the decision values of the non-noise sub-track segments and the time-space factor decision values, and generating a class cluster set; and determining a candidate representative trajectory set from the class cluster set based on the spatio-temporal local density and the maximum density, extracting continuous sub-trajectory segments from the candidate representative trajectory set based on spatio-temporal continuity constraints, and sequentially connecting end points of the continuous sub-trajectory segments to generate a representative trajectory. According to the method, the trajectory clustering effect is improved through space-time weighted segmentation and density peak trajectory clustering.

The invention discloses a short-term power load prediction method and system based on a CNN-Transform hybrid model, and the method comprises the steps: carrying out the data collection of historical load data and meteorological data of a power system, carrying out the data preprocessing of the collected data, and carrying out the coding of a periodic time feature, and obtaining periodic time coding information; local space-time features of the load data and the meteorological data are extracted by using a convolutional neural network, and hierarchical expression of the features is realized through a multi-layer convolutional structure during extraction; the local spatiotemporal features and the periodic time coding information are fused to obtain fusion features containing a load sequence, the long-period dependency relationship of the load sequence is modeled through a Transform network, global modeling of the fusion features is achieved through a multi-head self-attention mechanism, and a CNN-Transform hybrid model is obtained; a CNN-Transform hybrid model is used for prediction, and a load prediction result is output; according to the invention, the precision of load prediction and the generalization ability of the model are significantly improved.

The invention relates to the technical field of sea wave height prediction, and discloses a significant wave height prediction method and system based on spatio-temporal evolution multi-scale feature extraction. The method comprises the following steps: performing significant wave height prediction on input significant wave height grid data of a sea area to be predicted by applying a trained hybrid heterogeneous parallel double-convolution dynamic network; the method further comprises the steps of obtaining the effective wave height grid data, inputting the effective wave height grid data into the hybrid heterogeneous parallel double-convolution dynamic network for effective wave height prediction, and then outputting an effective wave height prediction sequence of a future time step. According to the method, multiple features of SWH spatio-temporal dynamic evolution can be extracted, and hierarchical spatio-temporal features from a local scale to a global scale and local spatio-temporal dynamic crossing from a coarse scale to a fine scale can be mined at the same time; by constructing a heterogeneous parallel double-convolution framework, the internal irregularity of the SWH field and the irregular relation between SWH field data are overcome, and meanwhile the capacity of capturing the invariant relation in the SWH field is kept.

The invention provides a factory three-dimensional digital twin model reconstruction and incremental updating method for industrial space intelligence, and the method comprises the steps: obtaining a factory multi-view basic image for a factory panoramic scene, so as to train a bifurcated structure neural implicit field network containing a radiation field branch and an SDF branch, and generating an initial three-dimensional digital twin base; acquiring factory real-time inspection images acquired by the inspection robot under different inspection poses, rendering the initial three-dimensional digital twin base by using a radiation field branch to obtain a virtual reference view, and performing difference analysis on the virtual reference view and the factory real-time inspection images to position a structural change area in a factory; determining an axis alignment bounding box for changing the physical range of the entity in the area, taking the axis alignment bounding box as an effective boundary of the local space patch, and determining matched radiation field network weight and SDF network weight according to the effective boundary; and adjusting the radiation field network weight and the SDF network weight, and updating the radiation field network weight and the SDF network weight to the network in an incremental updating manner to generate a factory three-dimensional digital twinning model based on the implicit three-dimensional geometric field.

The invention discloses an ultrasonic image denoising method based on variational mode decomposition and local space sparse fusion, which comprises the following steps of: firstly, adaptively optimizing key parameters of variational mode decomposition by using a grey wolf optimization algorithm to realize stable and efficient decomposition of an ultrasonic image; then, classifying the modal components according to the structural features of the modal components, and implementing differentiated denoising strategies for different types of modals to separate noise and reserve useful information; after modal reconstruction, a sparse expression method based on local space information is further introduced, according to the method, accurate boundary detection is carried out through gradient vector flow and gray scale proportion analysis, self-adaptive partitioning is carried out on an image according to boundary information, and finally sparse reconstruction is carried out through double dictionaries trained for different areas. According to the method, speckle noise in the ultrasonic image can be effectively suppressed, and meanwhile, the capability of keeping the edge and detail information of a tissue structure is remarkably improved, so that the ultrasonic image with higher quality is obtained.

The invention relates to a binding material single particle local spatial evolution analysis method, and belongs to the technical field of civil engineering binding material microstructure characterization. The method comprises the following steps: acquiring back scattering (BSE) and characteristic X-ray energy spectrum (EDS) images of a sample; selecting a target gel particle based on the BSE image and intercepting a local area; identifying the reaction edge of the target gel particle; performing equal-width stepped strip division inwards and outwards by taking the reaction boundary as a starting point, and storing the mask; carrying out image operation on adjacent masks to extract strips, and carrying out statistical analysis on gray feature and element feature changes of each strip; according to the method, surrounding phase interference is avoided through local analysis, whole-process analysis is realized by adopting unified image processing software, a programming basis is not needed, the technical threshold is reduced, the operation process is simplified, the uniformity of strip division and the reliability of an analysis result are ensured, and the method is suitable for large-scale popularization and application. And a simple, convenient and effective technical means is provided for revealing the reaction mechanism and quantitative reaction activity of the cementing material.

The invention discloses a space-time elastic weight consolidated edge federal continuous learning method, which is applied to a system comprising a server and a plurality of edge devices, is used for processing space-time heterogeneity time sequence data, and comprises the following steps: initializing training, broadcasting a previous time sequence global time Fisher diagonal matrix (the first time sequence is not broadcasted, the second time sequence is not broadcasted, and the third time sequence is not broadcasted) by the server; however, the global model needs to be randomly initialized and broadcasted); in the model training stage, based on local data, a global time Fisher diagonal matrix and the like, an edge device updates a local model through a loss function containing a time / space regular term, calculates a local space Fisher diagonal matrix, uploads the local space Fisher diagonal matrix, and then a server weights and aggregates the global model according to the data volume and issues the global model, and circulates until convergence; and in the global time Fisher diagonal matrix calculation stage, the equipment calculates a local time Fisher diagonal matrix based on a convergence model, and uploads and aggregates the local time Fisher diagonal matrix for the next time sequence. Historical data does not need to be stored, original data does not need to be transmitted, storage calculation / communication overhead is reduced, privacy is protected, and the model convergence speed and precision are improved.

The invention discloses a deep learning-based satellite remote sensing collapsible loess foundation evaluation method, which comprises the following steps of: acquiring an optical remote sensing image, a radar remote sensing image and an infrared remote sensing image of a target area, and preprocessing; collapsibility feature weighted data are screened, and feature weights are distributed; feature extraction is carried out through a local space exhibition structure and a multi-layer Transform structure of the collapsibility foundation evaluation network; collapsibility area self-supervised collaborative learning is carried out, and self-supervised training is carried out based on spatial correlation, pseudo labels and spatial consistency regular terms; and carrying out risk grade division on the target area, and outputting a collapsibility risk result of each spatial position. According to the method, multi-mode remote sensing and deep learning are fused, high-precision intelligent partitioning of the collapsible loess foundation is achieved, and the method has the advantages of being self-adaptive, low in manpower and high in spatial resolution.

The present invention makes it possible to effectively increase the density of a three-dimensional point cloud.　A plurality of clusters are generated by clustering three-dimensional point clouds acquired on the basis of the phase difference, or the time difference between the transmission time and the reception time, of a transmission signal and a reflection signal corresponding to each of a plurality of discretely arranged basic transmission points among a plurality of transmission points arranged in the form of a two-dimensional array. For each of the plurality of clusters, geometric information in a local space formed by the three-dimensional point clouds included in the cluster is calculated. For each of the plurality of clusters, a determination is made, on the basis of the geometric information, regarding whether to perform a density increase involving increasing the density of the three-dimensional point clouds included in the cluster by using another transmission point in addition to the basic transmission points corresponding to each point of the three-dimensional point clouds.

The invention relates to the technical field of big data analysis and application operation and maintenance, and discloses a multi-data fusion-based application operation and maintenance planning method, which comprises the following steps of: combining multi-source sensor data into a multi-dimensional state vector representing a local space-time state, and comparing the multi-dimensional state vector with a baseline vector representing a normal operation mode to generate a drift vector; according to the method, the stability of the correlation mode in the multi-dimensional data is analyzed, and then the stability of the modulus length and the direction of the drift vector in the time sequence is analyzed, so that potential risks which cannot be perceived by a traditional threshold value mode can be recognized. The focus point of risk recognition is transferred from isolated value out-of-limit to analysis of the stability of the correlation mode in the multi-dimensional data; the early gradual change risk formed by the synergistic effect of a plurality of physical quantities can be effectively identified, and the risk source can be traced based on the analysis process, so that the predictability and decision reliability of operation and maintenance planning are remarkably improved.

The invention belongs to the technical field of image processing, and relates to an egg surface microcrack detection method and system based on machine vision. The method comprises the following steps: acquiring a transmission image of an egg, performing spatial calibration, determining a geometric center and a long axis scale of an egg region, and establishing a polar coordinate mapping relation of each pixel point; according to the linear distance from each pixel point to the geometric center and the included angle relative to the long axis direction, the geometric sensitivity weight of each pixel point is evaluated, and the optical path distortion degree of the edge high-curvature area is represented; extracting contrast basic energy of the pixel points by using a local space window, and carrying out nonlinear evolution on a local gray gradient by combining with a geometric sensitivity weight to obtain an adaptive contrast energy evolution result; and coupling the local background variance and the geometric sensitivity weight, and calculating the crack identification confidence of each pixel point so as to carry out identification decision of the egg surface microcracks. According to the invention, extremely fine crack signals can be captured, and accurate detection of the microcracks on the surface of the egg is realized.

The invention provides a multi-view three-dimensional virtual-real fusion rendering method and related equipment, and relates to the field of computer vision, a target scene is divided into a space-time flow line or local space-time blocks by constructing a geometric field, an appearance field and a space-time flow field on a four-dimensional time-space domain, fragmentation caused by pure frame-by-frame rendering and scene specific rules is avoided, and the rendering efficiency is improved. The problem of multi-scene splitting is solved, and space-time importance distribution at each moment and under each virtual view angle is calculated through the updated feature vector and the attention weight corresponding to the updated feature vector so as to render a geometric field and an appearance field on a four-dimensional time-space domain. And the rendering picture quality is improved on the premise that the total calculation overhead is not obviously increased.

The invention discloses an abnormal event detection method and system based on traffic data and a storage medium, and belongs to the technical field of computer network security, and the method comprises the steps: obtaining original network traffic data, and carrying out the data segmentation, and obtaining traffic sample data; performing data preprocessing on the traffic sample data to obtain processed data; respectively performing time feature extraction and spatial feature extraction on the processed data to correspondingly obtain time feature data and spatial feature data of the network traffic; and performing feature fusion based on the time feature data and the space feature data, and performing anomaly detection on the fused space-time feature data based on a space-time attention mechanism. According to the method, the spatial relationship of bytes in the data packets and the time relationship between the data packets can be captured at the same time, and the overall performance of anomaly detection is improved by utilizing the time sequence dependency between the data packets and the spatial relationship in the data packets. A space-time attention mechanism can comprehensively consider global time sequence and local space information, and the accuracy and efficiency of abnormal traffic detection are improved.

The invention discloses a bolt missing grading detection method based on a global-local space position relation, and the method comprises the steps: obtaining an image containing a bolt missing defect, marking a bolt missing part, and dividing the image into a training set and a test set; constructing a bolt missing grading detection model, wherein the bolt missing grading detection model preliminarily identifies a small target containing a bolt and generates a prediction bounding box; analyzing and predicting a bounding box through a clustering algorithm, and adaptively selecting a local area with densely distributed bolts; a wavelet transform-based convolution module and a mixed local channel attention module are introduced to improve a bolt missing grading detection model, and the improved bolt missing grading detection model is used for detecting bolt missing defects in a local area; the training set and the test set are used for optimizing the bolt missing grading detection model, and the optimized bolt missing grading detection model is used for detecting bolt missing. The method can accurately identify and position the pin missing defect of the bolt.

The invention discloses a new energy output scene generation method and system based on tail risk enhancement, relates to the technical field of power scene generation, and aims to solve the problems of insufficient coverage of extreme scenes, spatial-temporal modeling distortion and lack of physical constraints in the prior art. The method comprises the following steps: constructing an improved generative adversarial network comprising an auto-encoder, a generator and a discriminator, extracting spatial-temporal features by adopting local spatial-temporal diagram convolution, and mapping the spatial-temporal features to a submerged space; a Jensen-Renyi divergence loss function is utilized to enhance the generation capability of a tail risk scene; designing a dual discrimination mechanism with an adversarial discrimination branch and a physical verification branch, measuring and counting distribution differences through a Wasserstein distance, and verifying physical feasibility based on a theoretical power curve of a fan; and finally, a target network is obtained through staged training, and a new energy output sequence data set with statistical completeness, space-time fidelity and engineering feasibility is generated according to real-time meteorological conditions. According to the invention, the coverage capability of the extreme risk scene and the engineering practicability of the generated scene are effectively improved.

The invention discloses a data division method, a large model reasoning method, devices, equipment and a medium, which are applied to the technical field of data processing, and the data division method comprises the following steps: based on historical access information corresponding to each key value cache, utilizing a prediction model to carry out prediction to obtain the probability that the key value cache associated with each key value cache is accessed in the future; dividing the key value caches based on the access frequency parameter, the time interval and the future access probability of each key value cache, and determining a hot data set and a cold data set, so as to store the key value caches in the hot data set to the video memory equipment with the fastest speed. According to the method, the access frequency is considered from the global aspect, the time interval is considered from the time local aspect, the future access probability is considered from the space local aspect, and the data is divided from different aspects, so that the key value can be obtained from the accurate hot data set in time during reasoning, and the accuracy of the data is improved. Therefore, the reasoning speed of the large model is improved.

The invention relates to the technical field of power construction automation, and discloses a power robot control method and system based on a visual voice action model.According to the method, a VLA visual voice action model is improved for a power construction scene, a full-connection layer of a feedforward neural network in an attention layer is enhanced through nonlinear transformation of the VLA visual voice action model; the method comprises the following steps of: replacing a combination of a deep separable convolutional layer and a full-connection layer with a combination of the deep separable convolutional layer and the full-connection layer, capturing'local space continuity 'implied in a Token sequence by using the deep separable convolutional layer, so that the feed-forward neural network obtains a local space convolution mechanism similar to a CNN local receptive field, and modeling global association by using the full-connection layer, so as to obtain the local space continuity of the local receptive field. According to the method, the spatial perception capability of the CNN and the global modeling capability of the Transform are effectively combined, the spatial understanding capability of the VLA visual voice action model is improved, and the VLA visual voice action model can more accurately control a power robot to complete a complex task in a complex power construction scene.

The invention relates to the technical field of space optimization, and discloses a container loading space optimization method based on robot collaboration, and the method comprises the steps: building a multi-dimensional physical model of a package through obtaining the point cloud data and weight distribution of the package; generating a loading scheme containing a target pose, a pressure tolerance threshold and a loading sequence based on a reinforcement learning algorithm; the mechanical arm generates a motor current-position composite control instruction according to a pre-trained grabbing dynamical model, and accurate grabbing is achieved; in the loading process, the pose of the parcel is monitored in real time through a Kalman filtering algorithm, when deviation exceeds a threshold value, a local space KD tree is constructed, a collision probability gradient field is calculated, and a compensation scheme meeting stability constraints is generated; and finally, updating the quad-tree index and feeding back to the reinforcement learning algorithm for online optimization. The problem that in the robot loading process, the grabbing force control is not accurate, and the efficiency is reduced due to execution deviation accumulation is effectively solved, and efficient and accurate automatic loading is achieved.

This invention relates to a method for extracting guided wavenumbers in variable thickness structures, belonging to the field of nondestructive testing technology. It utilizes an excitation signal to generate guided wave signals on the surface of the object under test. Based on a set window function and a set window length, the two-dimensional wavefield signal obtained by sampling the guided wave signal is divided into multiple local wavefield signals. A short-space Fourier transform is then performed on each local wavefield signal to obtain the amplitude-frequency-wavenumber array corresponding to each local space. A frequency range is then selected by summing the amplitude-frequency-wavenumber arrays of each local space, using the center frequency of the excitation signal as the midpoint. The weighted average wavenumber of each frequency point within the selected frequency range is calculated using the amplitude corresponding to the wavenumber sequence at each frequency point as the weight. The mean of the sum of the weighted average wavenumbers at each frequency point is then used as the wavenumber corresponding to the center position of each local signal space, and these values ​​are sequentially spliced ​​to obtain the wavenumber curve of the guided wave signal. This method solves the problem in existing technologies where the obtained guided wavenumbers cannot accurately reflect the thickness changes of variable thickness structures.

The invention discloses an interactive density denoising and reconstruction repair method for large-scale laser point clouds. The method comprises the following steps: S1, distinguishing noise points, sparse points and dense points for an original laser point cloud, deleting the noise points by using an interactive density denoising method, and storing deletion information; s2, generating a three-dimensional model by using greedy projection triangulation reconstruction; s3, deleting missing noise points or recovering mistakenly deleted sparse points by using an interactive density denoising method until the noise points are deleted and the mistakenly deleted sparse points are recovered; s4, dividing an overlapped local space, and performing hole detection and repair; and S5, searching a plurality of points closest to the original laser point cloud in the original laser point cloud, and taking an average value obtained by adding texture color values of the plurality of points as a color value of the vertex of the three-dimensional model. The method has the advantages that noise points, sparse points and dense points are distinguished by setting the point density threshold value, the colors and the shapes of the points are different, and the noise points are effectively deleted in an interaction mode.

1. The name of the design product: air purification unit (FFU). 2. The use of the design product: air purification for local space in pollution industry. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1.

The embodiment of the invention discloses a traffic network scheduling control method and system based on space-time big data and electronic equipment, and belongs to the technical field of control or regulation systems.The method comprises the steps that a first network state feature tensor representing a local space-time evolution mode is extracted from a space-time grid through a three-dimensional convolutional neural network, and aggregating node information on the dynamic weighting graph by using the graph attention network, generating a second network state feature tensor reflecting the global association and congestion propagation situation of the road network, fusing the two feature tensors, and inputting the fused feature tensors into an attention enhancement sequence model for multi-step prediction to obtain a future multi-period whole network traffic state. And cooperatively inputting the prediction result, the real-time state, the rule and the capacity constraint into the optimization model, generating a comprehensive scheduling instruction set including signal timing, lane control and path induction, and issuing and executing the comprehensive scheduling instruction set. According to the embodiment of the invention, holographic perception, prospective prediction and cooperative regulation and control of the traffic network are realized, and the traffic efficiency and scheduling response capability of the road network are improved.

The invention relates to the field of data storage, in particular to electronic equipment, a storage medium formatting method and device thereof and a program product. The method comprises the steps of determining a storage position of a PS stream file in a storage medium according to a preset positioning identifier of the PS stream file; and performing formatting processing according to a local space determined by the storage position of the PS stream file in the storage medium. And the local space is formatted, so that the workload of formatting processing can be effectively reduced, the formatting duration of the storage medium of the PS stream file structure is reduced, and the data formatting efficiency is improved.

The invention discloses a lightweight modulation identification method and system based on sparse graph construction, a medium, equipment and a product in the technical field of wireless communication and artificial intelligence. The method comprises the following steps: carrying out data preprocessing on a received wireless communication signal to obtain a complex field signal; carrying out modulation identification on the complex field signal by utilizing a lightweight modulation identification model; carrying out local feature extraction on the complex field signal by utilizing a feature extraction module to obtain amplitude and phase coupled local space-time features; performing dimension reconstruction on the local spatial-temporal features to obtain reconstructed local spatial-temporal features; according to the reconstructed local spatial-temporal features, performing graph convolution processing and feature enhancement by using a graph convolution network based on a multi-connection strategy to obtain enhanced features; and classifying the enhanced features by using a classifier to obtain a modulation type. The method can be widely applied to real-time signal identification tasks in Internet of Things data transmission nodes, low-power-consumption wireless sensor networks, unmanned systems and edge intelligent gateways.