103 results about "Streaming algorithm" patented technology

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention provides a bridge structure damage detection method based on image processing and CNN-LSTM, relates to the field of graphic image processing, and solves the problems that bridge vibration monitoring in the prior art is time-consuming, labor-consuming, high in cost and limited in precision, and adopts the technical scheme that the method comprises the steps of obtaining a recorded vibration video of a to-be-detected bridge structure; extracting the vibration video frame by frame to obtain bridge image data; detecting angular points by using a Harris algorithm, screening image data, and calculating vertical displacement of each feature point one by one; using a Lucas-Kanade optical flow algorithm to calculate the motion vectors of the feature angular points of the adjacent frame images; calculating the actual vibration displacement of the bridge; and carrying out standardization processing on the actual vibration displacement to obtain a two-dimensional matrix, inputting the two-dimensional matrix into the trained CNN-LSTM model, and outputting a final identification result of each type of damage. According to the scheme of the invention, high-precision bridge vibration displacement can be calculated, and efficient, accurate and high-precision bridge damage detection can be realized through the CNN-LSTM damage identification model.

The invention provides a sea level rise influence risk assessment method based on multi-source data fusion, a medium and a system, and belongs to the technical field of marine disasters. Basic feature data are obtained by constructing a multi-dimensional data acquisition network, nonlinear time series decomposition is performed by applying multi-scale wavelet analysis, and the risk assessment result is obtained by analyzing the nonlinear time series; establishing a prediction model based on a deep learning neural network; determining key vulnerabilities by using a minimum cut maximum flow algorithm; establishing a regional vulnerability scoring system; generating a comprehensive risk assessment matrix by using a Bayesian network ensemble analysis method; and finally, generating an evaluation report by using the coastline adaptability gating model. According to the model, a multi-head time sequence attention mechanism and bidirectional autoregression coding are fused, parameters are dynamically adjusted by adopting a regional adaptability gating weighting function, and accurate evaluation and dynamic tracking of sea level rising influence risks in a complex geographical environment are realized.

The invention discloses a river flow velocity fluctuation early warning method and system based on an optical flow algorithm. The method comprises the following steps: collecting continuous frame video data; processing the video data through a traditional optical flow algorithm and a deep learning optical flow algorithm, and performing densification processing on the sparse optical flow field data to obtain traditional optical flow densified data; calculating confidence mapping data of the sparse optical flow field data and uncertainty data of the deep learning optical flow field data; obtaining weight map data through a lightweight residual error optimized weight generation network; performing fusion processing on the traditional optical flow dense data and the deep learning optical flow field data according to the weight map data to obtain fused optical flow field data; and calculating flow velocity distribution data of the river surface by fusing the optical flow field data, and outputting flow velocity fluctuation early warning information data according to the flow velocity distribution data. According to the invention, the boundary retention capability of the traditional algorithm and the dense prediction advantage of the deep learning algorithm are fully exerted through the fusion of the two-path optical flow algorithm.

The invention discloses a construction period landslide disaster advanced prediction and rapid networking monitoring method, and the method comprises the steps: collecting synthetic aperture radar data, digital elevation model data and satellite precision orbit data of a monitoring region, carrying out the data preprocessing, and obtaining a single-view complex data image set; constructing an interference image pair connection graph, performing interference processing, and generating a differential interference graph and a coherence coefficient graph; performing interferometric phase unwrapping processing on the differential interferogram and the coherence coefficient graph according to a minimum cost flow algorithm to obtain earth surface deformation information; ground control points are selected, and the surface accumulated deformation quantity and the surface deformation rate are obtained; interpreting and delineating a potential deformation area, establishing an interpretation mark, selecting a typical deformation area, and extracting a time sequence deformation data set; and analyzing the time sequence deformation data set according to the neural network model to obtain a deformation prediction result and a disaster risk assessment result. According to the invention, the monitoring accuracy and prediction capability of landslide disasters can be improved.

The invention discloses a residual current detection system and method based on wireless synchronization and software confluence, and relates to the technical field of residual current detection. A host central processing device receives current sampling data with timestamps from a slave central processing device through a signal receiver to complete centralized receiving of multiple paths of current signals; after the host central processing device receives synchronous current sampling data with timestamps transmitted by the slave central processing device, aligning and synthesizing multiple paths of time-synchronized current signals through a software confluence algorithm, carrying out vector summation on the multiple paths of current signals based on a vector operation principle, and calculating the vector sum of residual current; and judging whether a grounding fault or an insulation defect exists according to whether the vector sum is zero, if the vector sum deviates from the zero value, judging that residual current abnormity exists, and giving an alarm or recording fault information. The detection system effectively improves the accuracy, reliability and applicability of residual current detection.

The invention relates to the technical field of electric connector weak light assembly vibration compensation alignment methods, particularly discloses an electric connector weak light assembly vibration compensation alignment method, and aims to solve the problems of low electric connector assembly positioning precision and high pseudo soldering rate caused by vibration in a weak light environment. The method comprises the following steps: monitoring the thermal radiation change of a vibration source in real time through an infrared thermal imaging sensor, and pre-judging a vibration acceleration vector in combination with a lightweight long-short-term memory neural network; synchronously triggering dual polarization angle imaging to respectively capture a housing reference mark and an enhanced terminal metal feature; the vibration data and the polarization image flow are fused in the field programmable gate array, a self-adaptive optical flow algorithm is adopted to resolve the displacement compensation amount, and a piezoelectric ceramic actuator is driven to carry out submicron order reverse displacement compensation; when the compensation amount exceeds a threshold value, a low-delay correction instruction is sent to the robot through an Ethernet control automation technology bus, and the steady-state error is ensured to be smaller than 0.3 micrometer by combining a strain gauge fine tuning feedback loop. According to the technical scheme, the assembling precision, efficiency and production line adaptability are remarkably improved.

The invention provides a reservoir water rain condition monitoring system and a monitoring method, the system constructs a dual monitoring system of physical observation reference and electronic automatic acquisition, a customized stainless steel water gauge and a multi-element acquisition device are deployed on key sections of a reservoir dam and a drainage basin, and a front-end edge calculation module is utilized to invert the flow in real time. And after the data is transmitted to the cloud platform, intelligent management and control are carried out through the four-pre functional module. The system displays the watershed situation based on a GIS graph, integrates an online flow measurement algorithm to generate a water and rain condition process line in real time, and realizes visual supervision by combining video monitoring and OSD data superposition. The core multi-target optimization scheduling model comprehensively considers flood control safety and power generation benefits, generates an optimal scheduling scheme through simulation deduction, and provides a closed-loop control interface. The problems of low monitoring precision, scheduling decision lag and the like in the prior art are solved, and the automatic management level and the water resource utilization rate of the reservoir group are remarkably improved.

The invention relates to the technical field of production scheduling and intelligent manufacturing, and provides an assembly line scheduling optimization method considering generalized priority constraints, which comprises the following steps: constructing an integer linear programming model, performing generalized priority relation enhancement and propagation on an input instance of the integer linear programming model, and improving the capacity of a workstation; calculating a simple lower bound based on task processing time, a lower bound based on the longest path of the priority graph and a lower bound based on a maximum flow algorithm, and taking the maximum value of the three as a final lower bound; defining an action space, defining a state space to represent the change of a current solution on an objective function value and a distribution balance degree relative to a global optimal solution and a local optimal solution, and designing a reward function to adaptively select a neighborhood operation; constructing an initial scheme based on the final lower bound and a Q-learning dynamic selection module, and performing batch movement iteration through neighborhood operation adaptively selected by the Q-learning dynamic selection module; and outputting an assembly line scheduling scheme meeting the generalized priority constraint based on a result of the batch movement iteration local search.

The invention provides a source network load storage cooperative control optimization method based on an improved selective transmission algorithm, and belongs to the technical field of power system dispatching. An energy storage device equivalent circuit model considering the temperature effect is established, and the state of charge is estimated in real time through Kalman filtering; a power system is modeled into a directed graph network flow model to set power balance constraints, an improved genetic algorithm based on tent chaotic mapping is adopted to solve an optimization model, a network flow algorithm is embedded in an iteration process to check feasibility, and premature convergence is prevented by adaptively adjusting crossover probability and mutation probability. And finally, outputting a Pareto optimal solution set, and selecting a scheduling scheme with optimal comprehensive performance according to an ideal point method. The technical problems of high system operation cost and insufficient new energy consumption rate caused by the fact that multi-objective optimization solution is easy to fall into local optimum in the source network load storage cooperative control optimization process are solved.

The invention provides an e-commerce brand promotion system based on social network influence propagation, and belongs to the technical field of e-commerce brand promotion systems. The system comprises a data acquisition module, a node identification module, a propagation dynamics model construction module, a promotion strategy generation module and an execution and feedback module. The data acquisition module obtains social and e-commerce data in a compliance manner and carries out standardization processing. The node identification module screens and classifies KOL through a multi-dimensional index, and determines potential propagation nodes in combination with K-means clustering; the propagation model module optimizes a'social network-SEIR 'model based on SEIR, and selects an optimal path by using a Dijkstrar + maximum flow algorithm; the strategy module generates a hierarchical KOL cooperation and user incentive scheme; the feedback module collects data per hour, dynamic adjustment is performed within 24 hours, and the method is executed in five steps corresponding to five modules. 30 days after application, the brand popularity is improved by more than or equal to 60%, the cost waste rate of small and medium-sized brands is reduced to 15% or below, the market sinking reach rate is improved to 38%, and popularization requirements of industries such as beauty makeup and the like are met.

The invention discloses a wire harness coating defect detection method and system based on visual identification, and relates to the technical field of visual detection. According to the system, aiming at the defect positioning problem caused by bending, torsion and illumination variation and shielding in detection of a wire harness in a complex form, an enhanced feature map capable of capturing wire harness space information and potential defects is generated by synchronously acquiring a multi-view image sequence, adopting a convolutional neural network to extract features and combining with normalization processing. According to the method, the central axis of the wire harness is tracked through iterative fitting, a unified axis model is constructed by fusing multi-view data through an optical flow algorithm, the radial offset of a defect area is calculated based on the model so as to carry out preliminary positioning, and finally, accurate three-dimensional reconstruction is carried out on the defect by adopting multi-view geometric constraint. According to the method, through multi-view information fusion and three-dimensional geometric optimization, the precision and robustness of wire harness defect detection in a complex environment are remarkably improved, and reliable spatial position data support is provided for automatic processing.

The invention discloses a video analysis-based early warning and monitoring method for children hyperactivity behavior and a storage medium, and the method comprises the steps: firstly, guaranteeing the comprehensive monitoring of children behaviors through employing a multi-channel video collection device, and providing a sufficient continuous data source for subsequent analysis; furthermore, the quality of monitoring data is ensured through the use of image noise reduction and segmentation technologies, and accurate motion features are effectively extracted through the combination of an optical flow algorithm and a background subtraction algorithm; further, the movement behaviors of the children are comprehensively analyzed by calculating behavior parameters such as movement amplitude, movement direction change and continuous movement time, and potential behavior abnormity is revealed; the multi-dimensional behavior feature vector obtained through behavior parameter analysis is compared with the standard template, the behavior type of the child is automatically recognized, early-stage symptoms of abnormal behaviors such as hyperactivity and the like are found in time, and a basis for early diagnosis and intervention is provided for behavior problems such as hyperactivity and the like of the child.

The application discloses a kind of based on dynamic receptive field and adaptive feature fusion plate defect detection deep learning method, to improve the detection precision and robustness of complex background and small scale defect under industrial environment.The proposed method is based on YOLOv10, dynamic receptive field module and adaptive feature fusion module are introduced in its architecture.Dynamic receptive field module uses local features and context information to dynamically adjust the size of convolution receptive field, to enhance the perception ability of different scale target;Adaptive feature fusion module realizes the weighted fusion of multi-layer features through multi-layer perception mechanism, improves feature expression ability and target recognition performance.The experimental results show that the method of the application is superior to existing mainstream algorithms in detection accuracy and real-time performance, has good engineering adaptability and industrial application prospect, and is suitable for furniture manufacturing, building material processing and other plate detection scenarios.

This application provides a method for configuring video stream algorithms, comprising: extracting visual features, spatiotemporal features, and semantic features of the input video stream; fusing these features to generate a first scene feature of the video stream; and generating a configuration algorithm for the video stream based on the first scene feature and a pre-constructed knowledge graph representing the relationship between scene features and algorithms. The technical solution of this application can effectively solve the problems of high configuration cost, poor scene adaptability, knowledge transfer gaps, and low configuration efficiency of video stream algorithms in smart city scenarios in existing technologies.

The invention discloses a cash flow calculation method and device and a storage medium, and relates to the technical field of financial science and technology, and the method comprises the steps: obtaining cash flow calculation parameters and obtaining business detail data from a distributed data layer in response to a cash flow calculation instruction; based on the cash flow calculation parameters and the business detail data, a cash flow index of the occurrence time point of each event in the future is calculated through a cash flow algorithm, a cash flow detail result is obtained, the cash flow algorithm is applied to a cash flow algorithm module, and the cash flow algorithm module supports containerization deployment or calculation cluster distributed deployment. The method can shorten the running time of mass data and improve the calculation efficiency.

The invention provides a dynamic disaster recovery method, system and device for cluster service shunting. The method comprises: obtaining load information of each server node in a service cluster, the load information comprising at least one of a CPU occupancy rate, a memory utilization rate, a disk occupancy rate and network port traffic; determining a load weight value of each server node according to the load information; service shunting information of the service cluster is acquired in real time, wherein the service shunting information comprises the number of server nodes for processing each service and a server node list; and updating the load weight value of each server node according to the service shunting information to obtain an updated load weight value of each server node, and generating alarm information and a service shunting scheme based on the updated load weight value of each server node. The problem that an existing intelligent flow distribution algorithm can dynamically adjust the flow distribution condition according to the load condition of cluster nodes and lacks disaster recovery processing means when the overall load of a cluster is high is solved.

The application discloses a UAV robust path planning method and system for uncertain dynamic environment, relates to the field of path planning, and realizes real-time tracking of dynamic obstacles by using EKF, and constructs a probability repulsive field based on Mahalanobis distance, effectively solves the sensing vulnerability and static model limitation of a traditional APF algorithm, and provides a robust dynamic obstacle avoidance guide for random tree expansion; secondly, a fuzzy logic adaptive step (FLC-AS) module is designed; the module intelligently adjusts the expansion step according to the risk degree of a local environment and an exploration stage, and realizes dynamic balance of exploration safety and search efficiency; comparison and analysis of the method and various mainstream algorithms show that the PFLS-RRT* algorithm has excellent adaptability in a complex environment, and achieves the best comprehensive performance in key indexes such as path quality, planning efficiency and robustness.

This application discloses a method, apparatus, and device for generating a scalable set of effective paths, relating to the field of path planning. The method includes: creating a family of Dial rules for filtering effective paths; selecting a rule from the Dial rule family as a compression rule, and filtering road segments in the traffic network that satisfy the compression rule to form a subnetwork; using the maximum flow algorithm in graph theory to determine the minimum edge cut from the start point to the end point in the subnetwork; determining a set of preceding effective paths from the start point to the preceding nodes of the minimum edge cut that satisfy the rule of increasing distance from the start point; determining a set of subsequent effective paths from the subsequent nodes of the minimum edge cut to the end point that satisfy the rule of increasing distance from the end point; and combining the preceding effective paths, the minimum edge cut, and the subsequent effective paths from the set of subsequent effective paths to obtain the set of effective paths from the start point to the end point. This application allows control over the number of effective paths.

This invention provides a method, medium, and system for assessing the risks of sea-level rise based on multi-source data fusion, belonging to the field of marine disaster technology. The invention acquires basic feature data by constructing a multi-dimensional data acquisition network, performs nonlinear time series decomposition using multi-scale wavelet analysis, establishes a prediction model based on a deep learning neural network, identifies key vulnerability points using the minimum cut maximum flow algorithm, establishes a regional vulnerability scoring system, generates a comprehensive risk assessment matrix using Bayesian network ensemble analysis, calculates the variable submatrix to extract risk change trends, and finally generates an assessment report using a coastline adaptive gating model. This model integrates a multi-head temporal attention mechanism and bidirectional autoregressive coding, and dynamically adjusts parameters using a regional adaptive gating weight function, achieving accurate assessment and dynamic tracking of the risks of sea-level rise in complex geographical environments.

The invention belongs to the technical field of power grid data monitoring, and discloses an intelligent power grid data monitoring method and device based on AI and RPA, and the method comprises the steps: constructing a power grid topological structure with equipment as nodes and lines as edges according to the actual connection relation of facilities in a power grid; a sensor is arranged on each node device in the topological structure to collect data such as voltage, current and temperature; generating a load data topological graph and a health data topological graph according to the collected data; utilizing a pre-trained AI model to respectively predict the load and the equipment health state; calculating a health-load comprehensive index based on a prediction result, and generating a load migration scheme by applying a minimum cost and maximum flow algorithm; and the migration scheme is automatically executed through the RPA system. According to the method, collaborative optimization of load distribution and equipment health is realized, the equipment with poor health condition is prevented from bearing overhigh load, the safety and reliability of power grid operation are improved, the operation and maintenance cost is reduced, and the service life of the equipment is prolonged.

The invention provides a maneuvering target correlation and tracking method under sparse observation based on a bimodal particle flow, and the method comprises the steps: constructing a core layer and exploration layer bimodal particle swarm with differentiated process noise, and employing a particle individual nearest neighbor normalization distance based on measurement noise as a soft gate correlation criterion. And the overall particle distribution is corrected by using a particle flow algorithm after exploration of particle capture observation. According to the method, the problem of deadlock caused by excessive confidence can be solved, accurate tracking of the sudden maneuvering target is always kept, the robustness under sparse observation can be remarkably improved, and breakpoint-free tracking can be realized. Moreover, the method does not need to maintain a plurality of filter models like an IMM algorithm, can achieve the coverage of a plurality of motion modes only through the adjustment of the particle noise distribution, is suitable for the real-time implementation of an engineering, and is more suitable for GPU parallelism.

The application relates to the technical field of satellite meteorological prediction, and discloses an optical flow-depth learning satellite multi-channel data extrapolation method. The method comprises the following steps: acquiring and preprocessing time-series satellite multi-channel data; a two-dimensional motion field is estimated by using a variational optical flow algorithm, preliminary space-time extrapolation is carried out in combination with a semi-Lagrangian algorithm and a mass conservation constraint; a deep learning model integrating space-time convolution and a recurrent neural network is constructed, and the preliminary extrapolation result and original deep features are jointly trained and predicted; two types of results are dynamically fused through an adaptive weight fusion mechanism; finally, the fusion result is corrected based on atmospheric dynamics and radiation transmission physical constraints, and a physically consistent multi-channel extrapolation field is generated. The method combines the advantages of physical motion estimation and data-driven learning, and improves the accuracy and rationality of satellite observation extrapolation from the minute level to the hour level.

The application discloses a low-voltage transformer area topology identification method based on current and electric quantity data, relates to the technical field of low-voltage transformer area topology identification, and comprises data preprocessing, a current algorithm module, an electric quantity algorithm module, a topology fusion module, a rehearsal and verification module and an output result module. The application provides the low-voltage transformer area topology identification method based on the current and electric quantity data, and aims at the deficiency of the prior art, and automatically infers the low-voltage transformer area topology connection relationship through the identification method.

The invention is applicable to the technical field of medical instrument data management, and provides a third-party platform medical instrument data management method, which comprises the following steps: data acquisition: in an official organization, acquiring a medical instrument unique identifier standard database in an xml format according to a published data interface; data conversion: converting the xml data into medical instrument database data according to a rule; data cleaning; updating the data; the database application comprises automatic auditing and auxiliary manual auditing. Data distribution is realized through intelligent matching, so that the product data auditing workload is greatly reduced; a shunting algorithm can be evolved, a final processing result of manual auditing shunting is used as a training set, shunting is more accurate, the manual auditing amount is less and less, and the working efficiency is continuously improved; and manual auditing is assisted to improve efficiency.

The invention relates to the technical field of hydrological monitoring and computer vision, and discloses a visual flow measurement method, device and equipment based on a large-scale particle image flow measurement algorithm and a medium, and the method comprises the steps: obtaining the monitoring video data of a to-be-measured river and camera calibration parameters; image frames in the video data are preprocessed; performing pixel displacement calculation on the preprocessed continuous image frames through a large-scale particle image velocity measurement algorithm to generate a surface velocity vector field, and converting the surface velocity vector field into an actual velocity; performing multi-criterion chained filtering on the surface flow velocity vector field to obtain an optimized flow velocity field; mapping the optimized flow velocity field to a river section grid, and performing integral calculation based on section water depth data to obtain a section flow time sequence; and generating a transparent vector layer based on the optimized flow velocity field, fusing the transparent vector layer with the original video frame by frame, and outputting a flow field visualization video. According to the invention, efficient, accurate and stable non-contact river flow measurement is realized, and the processing efficiency and the result reliability are improved.

The application discloses a multi-target path planning method of a power station inspection robot. Path planning is a key link for operation of the power station inspection robot system, and the performance of the path planning is closely related to equipment inspection efficiency and energy consumption ratio. Current mainstream algorithms generally have defects such as slow convergence speed, poor safety and low planning efficiency. Compared with the traditional method, the application significantly improves the path planning efficiency and path quality by simultaneously optimizing multiple targets, and enhances the stability and safety of the path.

The invention discloses a method for monitoring ships and aircrafts based on ArgusSAR, and belongs to the field of remote sensing image processing, and the method comprises the steps: obtaining ship and aircraft related SAR images, and obtaining a training set and a test set through marking and dividing; an ArgusSAR model containing a space encoder, a fine-grained feature aggregation encoder and a multi-dimensional perception attention decoder is constructed, and local and global feature extraction, multi-scale feature fusion and scale-space-task perception are realized respectively; and based on the training set and the multi-task joint loss function training model, inputting the test set into the optimal model to output the target position and category. The method is high in background interference resistance, can accurately detect dense and shielded targets, is adaptive to multi-scale targets, and is stable in cross-dataset generalization. Three data set tests show that core indexes are superior to mainstream algorithms such as a YOLO series, and a high-precision solution is provided for scenes such as maritime affair supervision and aviation safety monitoring.

The application relates to a Shenwei intelligent acceleration card system based on a Chiplet technology, a Chiplet architecture chip taking a Shenwei processor and a domestic FPGA as cores, being capable of supporting mainstream AI algorithm models and accelerating an inference process through cooperation of the powerful double-precision floating point and integer calculation power of the Shenwei processor, the powerful parallel computing capacity of the FPGA and the high-flexibility low-latency performance of the Chiplet architecture, and being applicable to domestic computers, servers, intelligent products and the like. The devices used in the application are all domestic, and have a higher self-controllable level; the Chiplet architecture designed with a PCIe interconnection bus as a core has the advantages of high transmission bandwidth, high calculation efficiency and easy realization of an inter-chip interconnection protocol; the Chiplet research and development idea based on the domestic general-purpose processor and the FPGA can be expanded according to application requirements to face different application fields.