2072results about How to "Reduce computational complexity" patented technology

The invention relates to the technical field of digital twinning, and discloses an AGV path optimization method and system based on digital twinning, and the method comprises the steps: carrying out the data standardization processing of AGV real-time coordinate data, environment obstacle distribution data and cargo storage position data, and constructing a storage environment state matrix; aGV task time parameters are calculated, and a task evaluation matrix containing task timeliness indexes and task priority coefficients is obtained; performing multi-target parameter calculation on the AGV motion energy consumption coefficient and the task weight coefficient, and establishing an AGV path optimization target function; performing path planning analysis by adopting a path exchange operator, a path insertion operator and a path inversion operator to generate an AGV initial path set; according to the AGV optimal path planning method and system, the flexibility and the optimization effect of path planning are improved, and the convergence speed and the quality of path optimization are remarkably improved.

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention discloses a vehicle trajectory prediction method, electronic equipment and a storage medium, and the method comprises the steps: obtaining vehicle trajectory data, and carrying out the classification processing and feature extraction of the vehicle trajectory data, so as to generate a standardized data set; based on the standardized data set, a vehicle trajectory prediction network model is trained, and the vehicle trajectory prediction network model comprises a time sequence feature extraction module, a space interaction feature extraction module and a decoder; the time sequence feature extraction module is realized based on an improved Mama architecture; the space interaction feature extraction module is used for processing by using a mixed framework of a Transform framework and a graph attention mechanism; the decoder processes the encoded features through a multi-head attention mechanism and considers the relevance between different time steps and different vehicles so as to generate a prediction result of the vehicle trajectory; and inputting the data of the scene to be predicted into the trained vehicle trajectory prediction network model to obtain a prediction result of the vehicle trajectory, and the method has significant advantages in the aspects of calculation efficiency, prediction accuracy and scene adaptability.

The invention discloses a laser SLAM (Simultaneous Localization and Mapping) method in a dynamic environment of a well mine, which relates to data processing and comprises the following steps of: performing mapping by utilizing a Cartograph SLAM algorithm; segmenting the PCD point cloud map by adopting an octree-based segmentation method according to a preset movement or deformation frequency threshold value of an object in each region in the well mining environment; adjusting the map resolution of each area in the PCD point cloud map according to the segmentation result; a change detection algorithm is used to carry out change detection on an area with object movement or deformation frequency exceeding a threshold value in the segmented PCD point cloud map, and new point cloud data of the change area is updated to the original PCD point cloud map in an increment mode; carrying out registration and fusion on the PCD point cloud map after local updating and an original PCD point cloud map; the global consistency is improved by aiming at the poor global consistency existing in the laser SLAM mapping of the well industry and mining in the prior art.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

The invention provides a lightweight underwater image enhancement method based on adaptive feature fusion, and the method comprises the steps: decomposing an underwater image enhancement task into a strong prior enhancement task and a fine-grained enhancement task through employing a lightweight underwater image enhancement network based on adaptive feature fusion, so as to make full use of the multi-scale information in an image; the strong prior enhancement task is used for extracting global and low-layer features of the image; the fine-grained enhancement task is used for improving local details and color recovery of the image so as to improve the visual quality; the lightweight underwater image enhancement network comprises a multi-scale feature aggregation module and a global channel attention module, the multi-scale feature aggregation module is composed of an adaptive feature fusion module and a segmented convolution feed-forward network, and the lightweight underwater image enhancement network is used for combining global and local information of features and adaptively adjusting importance of each channel through information association between channels.

The invention discloses a power distribution network distributed photovoltaic bearing capacity real-time evaluation method, which comprises the steps of determining a mathematical model for power distribution network photovoltaic bearing capacity evaluation, and constructing a Markov decision model for power distribution network photovoltaic bearing capacity dynamic evaluation based on the mathematical model; a soft actor-commentator SAC algorithm is used as a basic framework, graph sampling and aggregation GraphSAGE are used for improving the SAC algorithm, a graph deep reinforcement learning algorithm is constructed, the graph deep reinforcement learning algorithm is used for solving a Markov decision model, and a trained graph strategy network is obtained; carrying out the subsequent real-time evaluation of the distributed photovoltaic bearing capacity of the power distribution network through employing the trained graph strategy network; according to the method, the limitation that precise modeling is needed in a traditional method is effectively overcome, and a more accurate real-time evaluation result is provided.

The invention discloses a network security active defense method and system for detecting abnormal network behaviors. The method comprises the following steps: S1, constructing a virtualized isolation network environment through a software defined network technology; s2, deploying intelligent dynamic camouflage nodes in the virtualized isolation environment; s3, acquiring a disguised node attack behavior, and identifying a network abnormal behavior through a deep learning technology; s4, extracting network abnormal behavior features, and identifying potential threats through a deep learning technology; s5, a defense mechanism is automatically triggered, a honeynet is started, and abnormal behaviors and potential threats are isolated; and S6, tracing an attack path, and carrying out source positioning. The network security active defense system comprises a network construction module, a deployment module, an identification module, a honeynet, an isolation module and a tracing module. The disguised nodes are deployed through the deployment module to detect abnormal network behaviors, active defense of network security is realized through the honeynet, the isolation module and the tracing module, and the network security is improved.

The invention discloses a lightweight space-time traffic flow prediction method based on GAT and Mama, and relates to the technical field of intelligent traffic systems. According to the method, the feature expression ability of the model is improved through multi-dimensional feature learning (time, date and space-time) of an embedded layer, rich features of input data can be deeply captured, and the learning ability of the model is enhanced; a graph attention module is used to adaptively pay attention to the relative importance between nodes, so that the graph structure relationship in the data can be more effectively captured and utilized, and the analysis accuracy of the model on the spatial data is improved; the GAT-Mama module optimizes the time sequence analysis and spatial data processing capability, and the model can more accurately capture the dynamic dependency relationship between time points and between spatial positions through a multi-layer graph attention mechanism and residual connection; the GAT-Mama layer structure supports flexible adjustment, a user can adjust the depth of the model according to specific task requirements so as to adapt to spatio-temporal data prediction tasks with different complexities, and good adaptability is achieved.

The invention relates to the technical field of battery state monitoring, and discloses a sightseeing vehicle battery state monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the voltage, current and temperature data of a sightseeing vehicle battery, and carrying out the preprocessing, and obtaining a battery operation parameter data set; constructing a battery equivalent circuit model, and performing global and local two-stage optimization to obtain an optimal parameter set of the battery equivalent circuit model; constructing a state equation and an observation equation, and carrying out initialization setting on a covariance matrix of the triple forgetting factor adaptive extended Kalman filter to obtain a battery state observation model; priori state estimation is obtained through time updating operation, posterior state estimation is obtained through measurement updating operation, hierarchical estimation is carried out on the charge state and the polarization voltage of the battery, and the state characteristic quantity of the battery is obtained; according to the method, the reliability and the safety of the battery management system of the sightseeing vehicle are improved.

The invention discloses a power distribution network topology identification and state estimation method based on a message passing neural network and online deep learning, and the method comprises the steps: training a neural network through historical measurement data, building a power distribution network topology identification model, combining the obtained topology information with corresponding power grid measurement data, and constructing a graph data set; taking the obtained graph data set as a training set to train a message passing neural network to obtain a power distribution network state estimation model; and finally, inputting the real-time measurement data into a state estimation model of the topology identification model to obtain a real-time state estimation result under the section. According to the method, the power distribution network topology can be accurately identified under limited measurement data, accurate state estimation is provided for a power distribution system, and technical support is provided for subsequent safety assessment and optimized operation.

The invention relates to the technical field of synchronous positioning and map construction, and discloses a multi-sensor fusion SLAM method and system based on data quality comprehensive evaluation, and the method comprises the steps: carrying out the multi-dimensional quality evaluation of laser radar point cloud data and camera image data, and enabling the evaluation indexes to comprise the scene degradation degree, the view field integrity and the intensity information entropy of a point cloud, according to the point cloud data and the definition, self similarity and natural quality of the image, calculating the comprehensive weight of each index by adopting a subjective and objective weight fusion method, and dynamically selecting a laser radar pose, a visual sensor pose or an IMU integral pose increment as an initial value according to the comprehensive quality score of the point cloud data and the image data; and matching optimization of the point cloud and the map and global pose map optimization are carried out. According to the method, the sensor data quality is evaluated in real time, and the fusion strategy is dynamically adjusted, so that the positioning precision and robustness of the SLAM system in a complex environment are remarkably improved, and meanwhile, the calculation complexity is reduced.

The invention discloses a dynamic environment dense point cloud SLAM (Simultaneous Localization and Mapping) method and system based on YOLOv11 and ORB-SLAM 3. According to the method, real-time target detection and image segmentation technologies of a YOLOv11 model are integrated into an ORB-SLAM 3 framework, so that high-precision and robust visual SLAM in a dynamic environment is realized. A traditional convolution layer is replaced by using a balanced convolution method GSConv layer in a YOLOv11 model, and a traditional C2f layer is replaced by using a new feature fusion module VoVGSCS layer, so that a Neck structure of the YOLOv11 is improved, and a lightweight network model is realized. Experimental data prove that the pose estimation precision of the method in a dynamic environment is remarkably superior to that of an existing visual SLAM algorithm.

The invention discloses a face image super-resolution method and device of a diffusion model based on residual prediction, and relates to the field of image super-resolution, and the method comprises the steps: obtaining a to-be-reconstructed low-resolution image; a diffusion model based on residual prediction and a conditional noise predictor are constructed and trained, and a trained diffusion model and a trained conditional noise predictor are obtained; gaussian noise is sampled from standard Gaussian distribution to serve as residual noise images, a to-be-reconstructed low-resolution image, the residual noise image of each step and the corresponding step length are input into a trained conditional noise predictor, prediction noise of each step is obtained through prediction, and the low-resolution image is reconstructed. Iteratively executing a de-noising process of T steps through a reverse de-noising network in the trained diffusion model to obtain a restored residual image; and adding the restored residual image and the up-sampled low-resolution image to be reconstructed to obtain a restored high-resolution image. The image super-resolution method solves the problems that an existing image super-resolution method is complex in generation process and limited in detail recovery.

The invention discloses a long time sequence information prediction method based on a multi-scale double-source fusion network model, time domain and frequency domain features are modeled at the same time, the ability to understand a complex time sequence is enhanced, an efficient multi-scale attention module of cross-space learning can capture short-term and long-term dependency relationships at the same time, and the prediction accuracy is improved. The modeling capability of different time scale information is improved, dynamic weighted fusion of time domain and frequency domain features is realized by a gating mechanism, and the flexibility and robustness of the model are improved. And through standardization and size projection, feature deviation and memory occupation are reduced, model convergence is accelerated, and the overall memory demand is reduced. The efficient multi-scale attention module accurately captures features, weakens noise and abnormal value influences, and reduces unnecessary calculation and memory consumption. The gating mechanism dynamically adjusts the feature weight, preferentially processes key information, and reduces the memory demand.

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention provides a potential diffusion model-based building planning image generation method and system. The method comprises the steps of data preprocessing, potential diffusion model structure improvement, input condition optimization, model training, image generation and the like. The relevance between text description and architectural styles is enhanced by preprocessing data sets of architectural design images and urban planning images and introducing channel crossing and space crossing attention mechanisms. Meanwhile, an architectural style embedding vector, an architectural description text embedding vector and a geometric information embedding vector are combined as additional input conditions to ensure that the generated image contains complete information. In a model training process, a multi-scale strategy and depth separable convolution are adopted, so that the recognition capability and training efficiency of the model to a large scene are improved. The system comprises a data preprocessing module, a model optimization module, a model training module, an image generation module and the like, and can generate a high-resolution building image conforming to design specifications. The method and system can be widely applied to the fields of building design and urban planning, and design efficiency and accuracy are improved.

The invention discloses a rice spectrum stress index construction method and system, computer equipment and a storage medium, and relates to the technical field of rice stress classification, and the method comprises the steps: regularly shooting different groups of rice leaves to obtain original hyperspectral images, and carrying out black and white correction; extracting regions of interest from rice leaves under different stresses, calculating average spectral data and performing SG smoothing filter processing; using a generative adversarial network to extract 50 characteristic wave bands with the highest contribution degree; inputting the average spectral data corresponding to the characteristic wave band as model input data into a random forest classification model to obtain a model classification result; and meanwhile, screening the first five characteristic wave bands and corresponding weights to construct spectral indexes, obtaining stress indexes, dividing stress categories, and outputting stress reasons of the rice. According to the method, the nonlinear relationship in the data is more efficiently captured, the classification model performance is improved, the calculation efficiency is high, the adaptability is high, the dimensionality of the hyperspectral data is greatly reduced, and the model calculation complexity is effectively reduced.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.

The invention discloses a micro-expression recognition method and system based on a state space model and double-flow fusion, and relates to the technical field of expression recognition, and the method comprises the steps: extracting a starting frame image and a top frame image of a micro-expression continuous video frame sequence; inputting the initial frame image and the top frame image into a micro-expression recognition model based on a double-flow fusion network, extracting an optical flow image, and performing face key point detection and clipping operation on the top frame image and the optical flow image respectively; carrying out image block embedding operation to obtain texture features of the top frame image after cutting and motion features of the optical flow image; the two features learn the global dependency relationship and the local correlation between the image blocks from the positive direction and the negative direction through a parallel multi-layer bidirectional Mama fusion module, and deep image features and motion features are extracted after feature fusion; and the deep features of the two modes are interacted through a double-flow cross attention classification module, comprehensive features are learned, and a plurality of classifiers are combined to identify and output the micro-expression type more accurately.

The invention belongs to the technical field of time series prediction, and more specifically relates to a time series data prediction method based on a deep integrated learning model and high and low frequency separation. The method comprises the following steps: collecting time sequence data in a system, and preprocessing the obtained data; vMD variational mode decomposition is carried out on original time sequence data, and a time sequence is decomposed into K mode components with limited bandwidth; combining a maximum information coefficient method and a reconstruction error analysis method to determine an optimal decomposition mode number K; dividing high and low frequency components for all decomposed modes by using a zero-crossing rate and a center frequency; appropriate prediction models are established for the high-frequency component and the low-frequency component respectively; and superposing all modal prediction results to obtain a final time sequence prediction result. According to the method, multiple deep learning models are combined, so that the prediction method has higher flexibility and adaptability, and proper models can be selected for components with different characteristics for prediction, thereby improving the overall performance.

The invention provides a multi-degraded image restoration method based on self-adaptive prompt. The method comprises a two-stage hidden space diffusion model, and various degraded images can be flexibly processed. Specifically, the model generates a degradation cue by learning the difference between a degraded image and a clean image, and encodes the cue information in a hidden space. In the first stage, a degradation prompt is obtained through a degradation adaptive prompt learning mechanism as a key feature for guiding the diffusion model in the second stage to carry out image restoration. Finally, prompt information is integrated into a Fourier transform restoration network, and accurate restoration of different types of degraded images can be realized. The method not only improves the accuracy of image restoration, but also significantly reduces the calculation complexity, and is suitable for image restoration tasks in various real world scenes, including denoising, deblurring, defogging, rain removing, snow removing, raindrop removing and low illumination enhancement.

The invention relates to the technical field of channel estimation, and discloses an efficient OTFS sparse channel estimation method based on fast sparse Bayesian, which comprises the following steps: S1, forming a 2D data block in a time delay Doppler domain, and dividing the time delay Doppler domain; s2, acquiring a receiving signal of a time delay Doppler domain; s3, constructing a sparse sensing matrix, and optimizing the sensing matrix by using an adaptive particle swarm optimization algorithm; s4, setting a judgment condition for successful channel recovery; s5, channel posterior distribution and hyper-parameters are updated by adopting rapid sparse Bayesian based on Laplacian prior; and S6, judging whether iteration is continued or not according to a set judgment condition. According to the method, the sensing matrix is optimized by using the adaptive particle swarm optimization algorithm, the system performance is improved, the posterior distribution and the hyper-parameter of the channel are updated by using the fast sparse Bayesian method, the calculation complexity is reduced, and the calculation resources are saved.

The invention provides a lightweight potential diffusion model design method and system based on evolutionary neural architecture search, and the method employs a super-network technology to train a plurality of network architectures at the same time, combines an evolutionary genetic algorithm in an architecture search process, and trains a multi-layer perceptron as a score predictor, so as to improve the performance of the model. According to the method, the optimal architecture is efficiently screened out in a large-scale search space through the assisted evolution genetic algorithm, and the resource consumption and the calculation cost needed in the traditional architecture design process are remarkably reduced. According to the method, the pre-trained teacher model is introduced in combination with the knowledge distillation technology to supervise the training of the student model, and the potential diffusion model can be lightened while the stable performance of the potential diffusion model is ensured. Through the method, an efficient neural network architecture can be automatically designed, efficient deployment of the potential diffusion model can be realized, and a brand-new and low-cost solution is provided for optimization of the diffusion model.

The invention discloses a monocular depth estimation method based on hybrid CNN-Transformer, belongs to the aviation field and the computer vision field, and is applied to the field of environment depth perception in low-altitude flight of an unmanned aerial vehicle. Aiming at the problem that local-global information cannot be effectively extracted by a current depth estimation method, the design content mainly comprises the following steps: improving a visual Transform architecture based on an additive attention mechanism, optimizing feature interaction operation between image blocks, and improving the capability of capturing global semantic information; the method comprises the following steps: designing a deep network encoder mixed with a CNN-Transform module, and effectively extracting local-global feature information; a channel attention mechanism is introduced, and the feature fusion capability of the decoder is enhanced. The method is simple in process, an excellent test result is obtained through training on the flight data set Mid-Air of the low-altitude unmanned aerial vehicle, the extraction capability of local-global feature information is effectively enhanced, and the depth perception accuracy of the monocular depth estimation method in the flight scene of the low-altitude unmanned aerial vehicle is improved.

The invention relates to the technical field of logistics distribution data fusion, in particular to a data fusion method and system based on a logistics distribution knowledge graph. The method comprises the following steps: extracting original data and carrying out independent representation learning and unified representation processing; extracting uniformly expressed context information and carrying out dynamic perception embedding to obtain semantic enhancement features; fusing time-space and dependency features and physical characteristic features based on semantic enhancement features, performing message passing and relation inference by using a graph neural network, and finally determining a multi-mode relation set; global entity clustering and local feature fine matching are carried out based on semantic enhancement features, hierarchical entity alignment is carried out in combination with the multi-mode relation set, and an aligned entity set is obtained; and constructing an entity filling graph according to the aligned entity set, supplementing multi-dimensional relation attributes, and finally generating a logistics distribution knowledge graph. According to the invention, through a logistics distribution data fusion technology, a more comprehensive and more intelligent logistics distribution knowledge graph is realized.