165 results about "Multi targeting" patented technology

The invention relates to the field of commodity visualization, in particular to a commodity display interaction visualization method and device. The method comprises the following steps: collecting a multi-azimuth image of a commodity, carrying out three-dimensional texture modeling, and constructing a three-dimensional texture mapping model; performing material light rendering on the three-dimensional texture mapping model to generate a material rendering result; collecting an environment detection image of a commodity display environment, and performing environment illumination adaptation compensation on a material rendering result to obtain an illumination compensation rendering commodity; carrying out attribute information visual layout on the illumination compensation rendering commodity to obtain a commodity visual space; and carrying out interaction response animation analysis according to the commodity visualization space, carrying out multi-target parallel rendering, and executing commodity interaction visualization operation. The form and surface details of the commodity in the real world are accurately restored, the visual reality sense is improved, and the interactive experience feeling of browsing the commodity by a user is enhanced.

The invention provides a text code generation method based on multi-modal semantic embedding and a dynamic knowledge graph, and belongs to the technical field of artificial intelligence. According to the automatic code generation method based on multi-modal semantic embedding, the dynamic knowledge graph, constraint-driven code generation and context-aware repair, the semantic understanding precision and the code generation quality are remarkably improved by integrating the technologies of multi-head Transform, the graph neural network, reinforcement learning optimization, genetic algorithm sequence adjustment, the generative adversarial network and the like. According to the method, the knowledge graph can be dynamically constructed to enhance structured semantic modeling, high-quality codes conforming to specific industry standards are generated, functionality, efficiency and conciseness are considered, meanwhile, the iteration cost is reduced through an efficient error positioning and repairing mechanism, and the method is suitable for large-scale popularization and application. And the robustness and the flexibility of the system in diversified scenes are improved by utilizing adaptive weight optimization and multi-target balance.

The invention discloses a visual tracking method for a target aircraft target to identify a high-dynamic target, and relates to the technical field of dynamic visual tracking. According to the method, multi-source information of a visual frame stream and an event stream is fused, and the definition and stability of an initial envelope are ensured through the steps of infrared enhanced filtering, local contrast reserved filtering, multi-scale ROI screening, unified perspective correction and the like; in the trajectory generation and paragraph division process, two-norm fusion analysis is performed according to high-dynamic target maneuvering characteristics, trajectory mutation can be caught acutely, and the rationality of trajectory paragraph division is improved; a cross-level backtracking compensation mechanism further corrects envelope deviation caused by visual delay or jitter in high-speed motion, breakage and drifting of a target trajectory are effectively reduced, the accuracy of trajectory attribution can be kept under the condition of multi-target overlapping, target confusion is avoided, and layered early warning of target aircraft off-target behaviors is achieved.

The invention provides an advertisement putting dynamic game decision-making method and system based on multi-objective optimization, and the method comprises the steps: obtaining user behavior key data which comprises user visual focus position information; transmitting the user visual focus position information to a multi-target optimization model in a preset decision server, and generating multi-target optimization model input data; analyzing the input data of the multi-target optimization model and a preset game constraint condition by using the multi-target optimization model to generate a multi-target game decision parameter; and carrying out dynamic game deduction calculation based on the multi-target game decision parameters, and generating a dynamic advertisement position adjustment scheme so as to carry out advertisement putting dynamic game decision. According to the method, millisecond data is transmitted to the multi-target optimization model, the problem of strategy oscillation caused by data delay and target conflict in traditional advertisement decision making in a real-time attention scene is solved, and accurate dynamic matching of user focus drift and advertisement display is realized.

An additive manufacturing real-time process parameter optimization method based on reinforcement learning driving comprises the steps that firstly, a multi-mode online monitoring hardware platform is constructed, and a visible light camera, a thermal imaging camera and an acoustic sensor are integrated to sense the working condition of the additive manufacturing process in real time in an omnibearing mode; secondly, extracting key features, including visible light images, thermal imaging and acoustic signal branches, of modal data on line through a lightweight CNN-Transform hybrid network, and generating a unified low-dimensional state vector through convolution feature extraction and fusion of a Transform encoder; then, a reinforcement learning model is established, a multi-target reward function is designed in combination with the extracted feature data, a process jitter penalty term, an overheating penalty term and the like are covered, and dynamic mapping of parameters and performance is achieved. And finally, an online learning and real-time decision-making system is deployed, the trained strategy network is embedded into manufacturing equipment, self-adaptive adjustment and closed-loop control of technological parameters are achieved, and the stability and product performance of the additive manufacturing process are effectively improved.

The invention discloses a code generation method and system based on multi-objective reinforcement learning, and belongs to the technical field of digital integrated circuit design, and the method comprises the following steps: S1, generating an SFT training data set; s2, generating an MORL data set; s3, performing supervision and fine tuning on the model by taking the SFT training data set as input, so that the model masters the basic RTL code generation capability; s4, taking the MORL data set as input to train the model after supervision and fine tuning in the S3, and realizing multi-objective optimization; and S5, according to a user demand, describing a multi-target constraint through vector rewards, screening non-dominated candidate codes by using Pareto optimization to obtain an optimal RTL code, and outputting the optimal RTL code, vector reward details and a PPA equilibrium curve. According to the method and the system, basic constraints are met, and meanwhile generation of RTL codes with PPA indexes capable of being customized according to requirements is achieved.

The invention discloses a flood storage and detention area change pattern spot identification label generation technology based on style decoupling. The technology comprises the following steps: S1, constructing and preprocessing a change detection data set; s2, constructing a conditional diffusion generation network based on a decoupling encoder; s3, decoupling extraction and orthogonalization representation of content-style features are carried out; s4, constructing a multi-target training strategy and two-stage model training; s5, injecting and fusing style features based on cross attention; s6, cross-domain style migration and diversified label generation; and S7, based on label quality screening of physical and semantic double constraints, outputting a high-quality change detection expansion data set. Compared with the prior art, the method has the advantages that by introducing a content-style decoupling mechanism, the style and the content of the generated sample are independently and accurately controlled, and the change detection label which is consistent in ground feature layout, diversified in imaging style and accurately labeled at a pixel level is generated.

The invention discloses a multi-target tracking method for an offshore rain, fog and jitter environment, and the method comprises the steps: obtaining multi-target image data in the offshore rain, fog and jitter environment, and carrying out the target cutting and fusion of the multi-target image data based on a target enhancement segmentation strategy, and obtaining an enhanced sample image; mapping the enhanced sample image and target features in the multi-target image data into a candidate frame set of a pixel scale, and obtaining a sample data set containing adaptive Anchors according to the candidate frame set based on a clustering algorithm; performing model training on the multi-target detection network through the sample data set to obtain an optimal detection model so as to realize target detection; and defining a target state vector and a target observation vector according to a detection result, and realizing multi-target tracking under the marine rain and fog and jitter environment based on an improved Kalman filtering algorithm. The problems that in the prior art, the precision of multi-target detection under the marine rain and fog and jittering environment is insufficient, and a systematic solution for multi-target tracking under the marine rain and fog environment and the jittering scene is lacked are solved.

The invention provides a multi-target seamless tracking method and system based on active prediction and feature pre-fusion, and the method comprises the steps: obtaining real-time video streams of a plurality of intelligent cameras for registration, and obtaining a unified fusion video stream under a global geographic space coordinate system; a plurality of dynamic targets are recognized according to the fused video stream, a globally unique identity label is distributed to each dynamic target, a three-dimensional space prediction track and a virtual relay area of each dynamic target are determined by combining the fused video stream, and a relay intelligent camera is determined according to a shooting condition index; the relay intelligent camera is aligned with the corresponding virtual relay area, when the relay intelligent camera recognizes the dynamic target in the virtual relay area, the master control authority is switched to the relay intelligent camera for seamless tracking, and the virtual relay area where the dynamic target appears is actively predicted in a prospective mode for alignment of the relay intelligent camera; shooting view is ensured, and target losing is avoided.

The invention relates to a disaster situation video description generation method for an emergency disaster scene. The method comprises the following steps: carrying out frame-level sampling processing and multi-class target detection on a disaster video stream; calculating the closeness degree of the target in the space to construct an apparent visual image; constructing a hidden layer visual map; fusing the apparent spatial features and the latent spatial features to obtain fused features; splicing and mapping the alignment features and the fusion features to obtain cross-modal splicing features; and generating a word sequence from the cross-modal splicing features, and performing optimization training on the word sequence. According to the invention, based on an apparent space and latent space double-level visual correlation modeling mechanism, the multi-target structure relation modeling capability in a disaster video and the semantic accuracy of description generation are improved. According to the method, a visual association graph is constructed from two dimensions of dominant structure association and implicit semantic association between targets, and a graph convolutional network is introduced to perform high-level semantic feature extraction on a graph structure, so that the problem of description information missing caused by scene chaos and association complexity is effectively relieved.

The invention provides an endoscopic surgery video multi-target segmentation method based on a basic segmentation large model and mixed expert fine tuning. The method comprises the following steps: preprocessing a data set, establishing an SAM2 baseline segmentation network, constructing a hierarchical hybrid expert module, constructing a stage gating network, establishing an endoscopic surgery video segmentation network, training the endoscopic surgery video segmentation network, and automatically segmenting an endoscopic surgery video by the segmentation network. According to the invention, accurate segmentation of different operation scenes can be realized. The hierarchical hybrid expert module and the stage gating network are introduced, and the problems of scene difference and multi-organization segmentation commonly existing in endoscopic surgery video segmentation tasks can be well solved.

The invention discloses a multi-target real-time monitoring system and method for rearview mirror lens production and processing, relates to the technical field of automobile part manufacturing, and aims to solve the problem that an existing monitoring system cannot effectively distinguish false abnormity caused by dynamic environment interference in a rearview mirror lens crimping process and the real defect of the crimping process. The system comprises a data acquisition module used for acquiring first monitoring data, second monitoring data and third monitoring data of a crimping process in real time, the first monitoring data being visual image data of the crimping state of a lens and a supporting plate, the second monitoring data being visual image data of the crimping state of the lens and the supporting plate, and the third monitoring data being visual image data of the crimping state of the lens and the supporting plate; the second monitoring data is dynamic environment data of the crimping station, and the third monitoring data is crimping process operation data. The method has the advantages that the environmental interference and the coupling type of the rearview mirror lens crimping process are accurately identified, false abnormity and real defects are distinguished, and the monitoring misjudgment rate and the crimping reject ratio are reduced.

The invention relates to the technical field of power systems. The multi-target power system source-load interaction optimization scheduling method for new energy consumption is provided, and the method comprises the steps: carrying out the analysis processing of inter-region new energy output space-time complementation characteristics in a multi-space-time scale input data set through a preset standardized coordination interface unit, and generating a consumption complementation factor; performing dynamic correction processing on the local scheduling instruction based on the consumption complementary factor and the source-load interaction constraint condition, and generating a cross-regional power mutual aid plan; a preset improved multi-objective evolutionary algorithm is adopted to solve the four-dimensional optimization target model, and a Pareto frontier solution set is generated; and based on the Pareto frontier solution set and the cross-regional power mutual aid plan, processing is carried out through a preset multi-target balance decision-making unit, and an execution scheme is generated, so that the multi-target collaborative optimization capability is improved, the flexible load regulation potential is released, and the robustness of the cross-regional mutual aid plan is enhanced.

The invention discloses a GPU two-stage parallel implementation method for multi-target distance and speed ambiguity resolution, and relates to the technical field of radar ambiguity resolution, and the method comprises the steps: obtaining multiple repetition frequencies, randomly selecting the multiple repetition frequencies each time, and generating multiple repetition frequency groups; each repetition frequency corresponds to a plurality of plots, one plot is selected from the plots corresponding to each repetition frequency in the repetition frequency group each time to form a plot combination, a plurality of plot combinations corresponding to the repetition frequency group are generated, and identifiers of the plots in each plot combination are obtained; allocating a group of thread blocks to each trace point combination, and allocating a plurality of threads to each trace point; for the plurality of trace point combinations, performing coarse-grained parallel processing to obtain a minimum variance corresponding to each trace point combination; and for any trace point combination, performing fine-grained parallel processing to obtain a minimum variance corresponding to the trace point combination. According to the invention, the real-time performance of the defuzzification algorithm can be enhanced.

The invention provides a multi-target accurate retrieval method and system in a security video, and relates to the field of interdisciplinary application, and the method is characterized in that the method comprises the following steps: carrying out the standardized preprocessing of an original security video, and deploying an improved YOLOv8 model on a preprocessed key frame to carry out the high-precision multi-target detection. The method has the advantages that the appearance, motion and semantic complementary features of the target are synchronously extracted by improving the YOLOv8 model, cross-frame association and trajectory modeling are realized by adopting an enhanced DeepSORT tracker, and a compact feature index database and a three-level progressive retrieval mechanism are constructed in combination with a locality sensitive hashing algorithm; according to the method, the multi-target retrieval precision and efficiency in massive videos are remarkably improved, the target relevance and the complex scene semantic understanding ability are enhanced, meanwhile, the calculation overhead is greatly reduced through rapid approximate matching and a hierarchical retrieval strategy, and the requirements of security application for high accuracy, rapid response and real-time intelligent research and judgment are met.

This invention relates to the field of strategic planning technology, and discloses a strategy generation method and system based on anchor bolt mesh installation. The method includes: acquiring all anchor bolt positions and mesh sizes within the tunnel, and correcting the mesh sizes; digitally twinning the anchor bolt positions in a three-dimensional coordinate system to construct a multi-layered mesh digital twin model; and performing multi-objective strategy optimization on the multi-layered mesh digital twin model to generate a mesh installation strategy on the anchor bolts. This invention can automatically generate mesh installation schemes with high coverage, low loss, and strong stability, reducing manual intervention, material waste and rework rates, effectively improving construction quality and automation levels, and has significant engineering application value and promising prospects for wider application.

The invention belongs to the technical field of target tracking, and particularly relates to a human body gait data extraction method and system in a multi-target scene. The method comprises the following steps: correcting a mahalanobis distance formula for calculating spatial feature similarity in an original DeepSORT algorithm by utilizing a displacement field obtained by calculating dense optical streams of adjacent frames to obtain an improved DeepSORT algorithm, performing target tracking in a multi-target scene by utilizing the improved DeepSORT algorithm, and when a target is temporarily shielded or illumination suddenly changes, performing target tracking by utilizing the improved DeepSORT algorithm. The optical flow field can still speculate the target position through background pixel displacement, correct prediction deviation caused by target shielding or sudden illumination change, avoid tracking target loss or target ID jump, and realize stable tracking of multiple targets of pedestrians in a multi-target complex scene, so that the quality of gait data extraction is improved, and the precision of a gait recognition result is improved.

Systems and methods are described herein for select content for bid-processing and content placement online placement resources. A computer in an RTB bidding process transmits the selected content to an identified online resource that satisfies the various (and often competing) goal objective thresholds and budget thresholds. The computer transmits the content to the identified online resource, which forwards or transmits the campaign content for display at a user interface of an end-user. The computer analyzes historical data to evaluate the performance of content campaigns against multiple user-selected performance goals and priorities to determine whether to bid for content placement at a given online resource. The computer may generate a blocklist to reject placements at underperforming online resources. The computer may perform optimization functions to determine optimal bid prices that balance the goal objective thresholds and budget threshold and determine whether to bid for the placement.

The application discloses a structure design method of a multi-target cooperative optimization PGNAA device, and the method comprehensively considers the influence of multiple performance indexes, combines a Monte Carlo method, introduces a multi-target cooperative optimization mechanism with constraints, and thus obtains an optimal structure module combination of the PGNAA device meeting engineering application requirements, and can effectively improve the measurement and analysis level of the PGNAA device on samples.

The invention discloses an automatic marketing strategy generation system based on intelligent service, and relates to the technical field of intelligent marketing, and the system comprises a basic template layer, a dynamic adaptation layer, a creative generation layer, and a multi-target strategy selection layer. Through a constructed hierarchical strategy engine of a basic template layer, a dynamic adaptation layer, a creative generation layer and a multi-target strategy selection layer, a single strategy generation mode of a traditional fixed template is broken through, the basic template layer uses an industry marketing knowledge base to provide a standardized strategy generation starting point, the dynamic adaptation layer accesses multi-source heterogeneous data in real time, and the multi-target strategy selection layer is used for providing a standardized strategy generation starting point. According to the method, a creative generation layer is established, strategy parameters are dynamically corrected, market changes can be quickly responded, the creative generation layer automatically generates personalized marketing content by means of a multi-modal generation technology, and a gradient type strategy generation mode is formed from basic framework establishment to real-time parameter adjustment to creative content output through the dynamic layered architecture. And a brand new technical path is provided for marketing strategy generation.

The invention provides an old city bungalow area resident translation and moving decision-making method based on multiple agents. The method comprises the steps of collecting plot information of an old city bungalow area and constructing a data set, establishing a resident agent and a developer agent and setting behavior rules and action spaces of the resident agent and the developer agent, setting a learning method of the developer agent as multi-target hierarchical reinforcement learning, setting multi-target hierarchical rules respectively, and performing multi-target hierarchical reinforcement learning. And setting a reward mechanism of reinforcement learning, running all agents and performing data simulation, dynamically adjusting a relocation strategy in the simulation process until a convergence condition is met, outputting data and forming a result list. Compared with the application of the prior art, the multi-agent-based translation and moving decision-making method for residents in the one-storey area of the old city innovatively introduces the agents into a new scene of stock development, helps developers to make a more scientific moving decision, and can obtain a win-win balance result of the developers and the residents.

The multi-detection-view-angle target matching method based on the error direction consistency comprises the steps that multiple targets are detected at different angles through multiple aircrafts, and images of the multiple targets at different detection view angles are obtained; detecting each image by adopting a target recognition algorithm, and calculating a sight angle of a target in a camera coordinate system; acquiring a sight angle under an inertial coordinate system, and acquiring position and attitude angle information of multiple aircrafts; calculating direction vectors of the target under different detection view angles and direction vectors among the multiple aircrafts; constructing a target matching model under multiple detection view angles by using the characteristic that the direction vector of the same target under different detection view angles and the corresponding aircraft are in the same plane and have a unique intersection point; according to the characteristic that the error direction of the current multi-target data frame has consistency, target rough matching under multiple detection view angles is completed, and a matching threshold value is calculated; and performing differential matching with the multi-target rough matching value under each detection view angle based on the matching threshold, and coding the successfully matched target.

The invention discloses an elevator safety guard method and system based on cloud side end fusion of multi-target detection, and relates to the field of target detection. Image data are collected in real time through a camera in an elevator, real-time reasoning is conducted through a trained target detection model, and electric vehicle and storage battery targets are detected; once illegal behaviors are detected, an alarm is given immediately, an elevator door is controlled to be kept open, and meanwhile the face detection and feature extraction process is started; face key point features are extracted through a dlib library, Euclidean distance matching is carried out on the face key point features and a preset resident face database, and identity recognition is achieved; a detection result and face feature data are uploaded to a cloud management platform, alarm information is generated and pushed to a property management APP, and property management personnel can remotely check the early warning accuracy and take corresponding measures. According to the method, the false alarm rate is effectively reduced, the recognition precision and the response speed are improved, and a complete treatment closed loop is formed.

The invention discloses a power CPS network attack intelligent positioning method and system based on a multi-target evolution CNN. The CNN is used as a multi-label classification model for power CPS network attack positioning; designing a variable-length hybrid coding strategy by taking architecture parameters such as a convolution layer number, a filter number, a convolution kernel size, a pooling kernel size, a full-connection layer number, a full-connection layer neuron number and a discarding rate in the CNN and hyper-parameters such as a learning rate, a batch size and an optimizer as decision variables; the method comprises the following steps of: taking the minimization of the number of model parameters of the CNN and (1-macro average F1 score) as an optimization objective function, designing a Pareto leading edge selection mechanism based on non-dominated sorting, and a block single-point crossing strategy and mutation operation oriented to hybrid coding individuals, and carrying out offline multi-objective optimization iteration to obtain a compromise optimization CNN giving consideration to attack positioning precision and model lightweight. And on-line deployment is carried out, and accurate detection of the intelligent positioning system for the power CPS network attack is efficiently realized.

The invention discloses a multi-objective dynamic optimization method and device, electronic equipment and a storage medium, relates to the technical field of control, and integrates structured time sequence operation data and environment information of a thermal power plant, and outputs a prediction load curve including a confidence interval during unit load prediction, thereby effectively reducing prediction errors. A control parameter set value can be generated in combination with the prediction curve and a real-time unit operation state, and parameters are prevented from deviating from an optimal interval through safety range verification and smooth processing, so that the limitation of an existing single target optimization algorithm is eliminated; the technical effects of improving the unit load prediction precision, ensuring that the control parameters are in the optimal operation interval, improving the power generation efficiency, reducing the carbon emission intensity, accelerating the frequency modulation response speed and meeting the key index requirements of the novel power system on the thermal power plant are achieved.

The application discloses a multi-target particle swarm optimization-based multi-base SAR space configuration design method, which comprises the following steps: firstly, setting multi-base SAR space configuration design parameters, modeling a multi-base SAR echo model, and then performing multi-base SAR imaging wave number spectrum analysis; secondly, modeling a space configuration optimization design problem, inputting space configuration optimization design problem system parameters and configuration parameters, initializing MOPSO, then updating the speed and position of the particle swarm, and finally outputting the optimized configuration through iterative updating. The method of the application converts the space configuration design problem into a constrained multi-target optimization problem, introduces a multi-target particle swarm optimization algorithm for solving, realizes high-resolution imaging through the optimized space configuration, breaks through the strict limitation of the multi-base SAR space configuration, and can complete the optimization design of the relative positions and flight speed directions of multiple radar platforms under certain time and system resource conditions, so that higher-resolution images can be obtained within a certain observation time.

The present application relates to a kind of press free forging large-scale drop hammer multi-parameter multi-field intelligent optimization method and system, through the three-field coupling modeling of heat-force-microstructure, depth nuclear learning agent model and the multi-objective evolutionary algorithm guided by digital twin, realize the collaborative optimization of process parameters, die geometry and microstructure, improve optimization efficiency, reduce the number of trial die;While building real-time closed-loop control system, realize the grain size uniformity of forging, forming load reduction and production cycle shortening, improve process stability and die life.

The embodiment of the invention discloses a multi-target labeling method and device, equipment and a storage medium. The method comprises the steps of obtaining target video data, wherein each target object appearing for the first time has an initial accurate label and a labeling box in a corresponding video frame; detecting each target object appearing in each video frame in the target video data to obtain a detection frame marking a detection result; the target video data, the initial accurate labels, the labeling boxes and the detection boxes are input into a multi-target tracking model together, target objects are tracked and matched through the multi-target tracking model, the highest priority is set for the accurate labels, first labeling labels corresponding to all the target objects in all the video frames are obtained, and the accurate labels comprise the initial accurate labels and the detection boxes; the first labeling labels corresponding to the same target object are the same and are initial accurate labels corresponding to the target object. According to the technical means, the technical problem that the labeling accuracy of a multi-target tracking model is limited when tracking labeling is carried out on a plurality of specific targets in the video data is solved.

The invention relates to the technical field of multi-target sample automatic labeling, in particular to a multi-target sample automatic labeling method based on a picture text large model. The method comprises the following steps: S1, detecting a picture text by using an open set target detection algorithm Ground DIN0, and outputting a corresponding detection frame in a picture by using a model weight; s2, de-weighting the detection frame by using a non-maximum suppression method, and reserving the category with the maximum confidence in the Grouping DINO output target categories and the detection frame; and S3, storing the detection frame information and the label information in a format required by a labeling platform. According to the multi-target sample automatic labeling method based on the picture text large model, the multi-target samples in the picture are effectively and accurately labeled automatically through the open set target detection algorithm GrondingDIN0, and only a detection frame which is not too close needs to be slightly adjusted, so that the workload of labeling personnel is greatly reduced, and the labeling efficiency is improved. And the marking accuracy is improved.