485 results about "Scene matching" patented technology

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention relates to the technical field of virtual power plant regulation and control, and provides a virtual power plant multi-scene cooperative regulation and control method and system for power supply insurance, and the method comprises the steps: periodically obtaining the supply and demand time series data of a virtual power plant, and carrying out the supply and demand fluctuation characteristic analysis of the supply and demand time series data of the virtual power plant, and generating supply and demand fluctuation time series data; according to the supply and demand fluctuation time sequence data, scene matching analysis is carried out based on a pre-constructed multi-scene regulation and control strategy library to obtain an initial scene regulation and control strategy; performing scene adaptability simulation analysis on the initial scene regulation and control strategy, and optimizing the initial scene regulation and control strategy according to a simulation analysis result to obtain a target scene regulation and control strategy; and performing layered and partitioned disassembly on a regulation target in the target scene regulation strategy to obtain a layered and partitioned regulation target, and executing virtual power plant regulation according to the layered and partitioned regulation target. According to the method, the collaborative optimization capability and the operation toughness of the virtual power plant in a complex and uncertain environment can be remarkably improved, and reliable technical support is provided for power supply insurance.

The invention relates to the technical field of artificial intelligence, in particular to a scene application operation maintenance platform based on an artificial intelligence model, which comprises a reasoning state sensing module, a load balancing scheduling module, an intelligent resource allocation module, a deployment configuration adjustment module and a scene feedback integration module. According to the method, by sensing and reasoning process resource consumption and response time migration trend, abnormal tasks caused by resource fluctuation can be accurately recognized, the state monitoring sensitivity is improved, task allocation records are analyzed to construct conflict structures, the resource conflict recognition and visualization capability is enhanced, the model operation level and the GPU vacancy rate are combined, a task migration path is optimized, and the task migration efficiency is improved. Node resource balance is realized, storage frequency, memory margin and network delay are fused during deployment, migration stability is guaranteed, drift tasks are identified through response logs and call records, an operation and maintenance list is generated, operation and maintenance accuracy and multi-scene matching capability are improved, and reasoning task stability and resource scheduling intelligent level are enhanced.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention discloses a gamepad energy-saving control method and system based on intelligent analysis, and the method comprises the steps: obtaining the operation monitoring information of a gamepad, and carrying out the preprocessing of the operation monitoring information of the gamepad, and obtaining the multi-mode fusion features of a target gamepad; constructing a user operation analysis model, and analyzing the operation behavior of the target user using the gamepad to obtain user operation analysis information; constructing an operation state transition matrix based on the multi-modal fusion features of the target gamepad, and predicting the operation intention of the target user; constructing an energy-saving control strategy database, acquiring energy-saving control parameters matched with the current operation scene from the energy-saving control strategy database, and establishing a dynamic regulation and control mechanism to dynamically regulate and control the energy-saving control parameters; and if the matched energy-saving control parameter combination does not exist in the energy-saving control strategy database, making an energy-saving control strategy. Intelligent energy-saving control of dynamic power consumption regulation and control is realized, so that the energy efficiency performance of the gamepad and the user experience are improved.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention discloses an unmanned aerial vehicle-satellite scene matching method based on two stages. Precise scene matching and positioning are completed from coarse granularity to fine granularity. An existing satellite base map retrieval task is used as a first-stage task, and a local feature point matching method is introduced in a second stage. Compared with an existing method, the method has the advantages that the matching robustness in a complex scene is remarkably improved, meter-scale accurate positioning of the unmanned aerial vehicle can be realized on an existing satellite map, and a new universal solution is provided for the field of scene matching.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a scene simulation generation method which comprises the following steps: collecting text data, user data and environment data, forming a training data set, and constructing a scene simulation model based on the training data set; extracting target key information from the target text data, generating a target user portrait in combination with the target user data, and generating a scene matching strategy according to the target user portrait and the target key information; and inputting the target key information, the target user portrait and the scene matching strategy into a scene simulation model to generate a target scene simulation scheme. The text data, the user data and the environment data are integrated and labeled and classified, the scene matching strategy is generated in combination with the user portrait and the target key information, the scene simulation scheme conforming to the user characteristics is generated by utilizing the scene simulation model, accurate matching of terms and user requirements is realized, and the user experience is improved. And the user can understand the application range and effect of the clauses.

The invention belongs to the field of road vehicle control systems, and relates to a vehicle trafficability risk judgment method and system fusing driver ability and confidence. The system comprises a driving behavior and environment data acquisition module, a scene matching module, a traffic capacity estimation module, a traffic capacity scoring module, a driving behavior feature analysis module, a traffic confidence scoring module, a trafficability risk calculation module and a database. The scene matching module is used for carrying out similarity calculation on the basis of a historical traffic sample set and the structural features of the current traffic scene and screening similar samples; the traffic capacity estimation module is used for carrying out weighted fusion on the screened traffic capacity and the calculated matching weight to obtain a traffic capacity estimation result; and the trafficability risk calculation module is used for correcting the original trafficability risk based on the structure complexity and the vehicle performance by using the traffic capacity score and the traffic confidence score. According to the system, personalized and dynamic adjustment of traffic risks is realized, and the accuracy of trafficability risk prediction is improved.

The invention discloses a vibration space-time diagram event identification method based on distributed optical fiber sensing. According to the invention, through combination of spatial-temporal feature fusion and a deep learning technology, the accuracy and robustness of event identification are significantly improved. The method comprises the following steps of: firstly, integrating space-time dimension information of an optical fiber vibration signal into two-dimensional characteristics by a space-time diagram, and providing richer context association for a model; through introduction of the channel attention and space-time attention module, key features can be adaptively focused, noise interference can be suppressed, and the adaptability of the model to a complex background is enhanced. Meanwhile, the dynamic loss function optimizes the training process of different scale targets by dynamically adjusting the loss weights of the bounding box and the mask, and solves the problem of performance reduction caused by target scale difference in the traditional method. The method has strong generalization, can adapt to various optical fiber types and monitoring scenes through data enhancement and adaptive parameter adjustment, and reduces dependence on data annotation and scene matching in actual deployment.

The invention relates to the technical field of scene matching navigation, and provides a multi-mode scene matching navigation positioning method and device for a self-adaptive variable-structure unmanned aerial vehicle. According to the method, geographic coordinates corresponding to all pixel points in an aerial image are determined according to pose information in the aerial image of the unmanned aerial vehicle, and the geographic position of the unmanned aerial vehicle with errors is corrected based on difference information between the pixel coordinates of the central point of the aerial image of the unmanned aerial vehicle and the geographic position corresponding to the center of a camera. Based on the corrected geographic position of the unmanned aerial vehicle, determining a reference image pixel coordinate corresponding to the angular point pixel coordinate of the aerial image, and obtaining a simulated aerial image under the view angle of the unmanned aerial vehicle through perspective transformation so as to determine a homography transformation matrix; according to the method, the satellite reference image pixel coordinates of the interest point in the aerial image are determined based on the homography transformation matrix and the perspective transformation matrix, and finally the longitude and latitude of the interest point are determined, so that the limitation of the traditional method in view angle difference compensation, computing resource constraint and cross-modal processing is solved, and the universality, robustness and positioning accuracy of the system are improved.

The embodiment of the invention provides a three-dimensional Gaussian scene reconstruction method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly, obtaining a plurality of three-dimensional Gaussian distributions of a target scene; then, obtaining a two-dimensional projection weight of each three-dimensional Gaussian distribution, and selecting a key Gaussian from the plurality of three-dimensional Gaussian distributions based on the two-dimensional projection weights; next, performing Gaussian splitting on the key Gaussian to obtain a plurality of split Gaussian distributions, and performing depth re-initialization based on the plurality of split Gaussian distributions and the plurality of three-dimensional Gaussian distributions to obtain a plurality of dense three-dimensional Gaussian distributions; and finally, selecting a plurality of reserved Gaussian distributions from the dense three-dimensional Gaussian distributions based on the two-dimensional projection weights of the dense three-dimensional Gaussian distributions, and performing scene reconstruction based on the reserved Gaussian distributions to obtain a reconstructed scene matched with the target scene. Therefore, the overall efficiency of three-dimensional scene reconstruction and the compactness and quality of the finally reconstructed scene are obviously improved.

The invention provides a multi-round dialogue question and answer method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current question, historical dialogue information and / or a user portrait of a user; obtaining a plurality of predefined scenes in the scene information configuration file, performing scene matching on the current problem, and determining predefined information of a target scene corresponding to the current problem; on the basis of a pre-constructed inheritance model, according to the predefined information of the target scene, whether the historical dialogue information and / or the user portrait need to be inherited or not is judged, if yes, the current question is complemented in combination with the predefined information of the target scene, the historical dialogue information and / or the user portrait, and the complemented current question is output; wherein the inheritance model is obtained by training based on the sample question, the sample predefined information of the corresponding sample scene, the sample dialogue information and / or the sample user portrait. The scene adaptability is high, the model training efficiency is improved, and the model training cost is reduced.

The invention provides a multi-modal feature vector fused vehicle cross-lens ReID matching method and system, and relates to the technical field of vehicle identification, and the method comprises the steps: collecting a vehicle image through a camera, and extracting triple features, including a bottom-layer visual feature, a middle-layer semantic feature and a high-layer character feature; in combination with vehicle space-time metadata at adjacent acquisition cameras, generating space-time trajectory feature vectors, and capturing a time sequence rule of a vehicle driving path; the missing features are complemented by adopting an AI generation technology, and the complementation precision is optimized through adaptive learning; inputting the triple feature, the time sequence rule and the complementation feature into a Transform fusion network to generate a fusion feature vector; calculating the cosine similarity of the to-be-matched vehicle according to the fusion feature vector, and carrying out cross-border head matching; according to the method, the problem of insufficient distinction degree of single-modal features is solved, the track feature vector is generated by combining the space-time metadata of the camera, the driving time sequence rule of the vehicle between different lenses is captured, and the space-time constraint of cross-scene matching is enhanced.

The invention provides an efficient data retrieval method and system oriented to a large language model. The method comprises the following steps: determining an intention vector set matched with a user intention; determining a specified number of candidate text fragments with the highest correlation with the user intention in an index database; obtaining multi-dimensional feature evaluation data of each candidate text fragment, and constructing a multi-dimensional feature numerical matrix of each candidate text fragment according to the multi-dimensional feature evaluation data; according to the multi-dimensional feature numerical value matrix, selecting selected text fragments from the candidate text fragments; sorting the selected text fragments, and sequentially performing scene matching on the selected text fragments and a pre-cached high-frequency query scene according to the sorting, so as to extract cache content matched with the target high-frequency query scene from the cache as a query result when a matching result represents that the current query scene accords with the target high-frequency query scene. The invention provides an efficient data retrieval method, and the quality and precision of data retrieval are improved.

The invention discloses a multi-scene term translation model training method and processing method based on a large model, and the method comprises the steps: firstly constructing a term-scene-translation triple data set, and formatting the term-scene-translation triple data set into a training sample containing an instruction, context input and expected output through a structured term translation instruction set; then, the samples are used for fine tuning of the basic large language model, and a preliminary adaptive model is obtained. And finally, introducing a reinforcement learning method based on a verifiable reward mechanism to optimize the fine tuning model, and iteratively improving the performance of the model in the aspects of scene matching degree, specification conformity, cross-scene consistency and the like by calculating a reward value. According to the method, the problems that a traditional term translation model is poor in flexibility and insufficient in specialty are effectively solved, and the multi-scene term translation which is high in accuracy and conforms to specific industry specifications can be automatically generated.

The invention relates to the technical field of dangerous cargo management, in particular to an intelligent compliance verification system for dangerous cargo boxing operation. Comprising a data acquisition preprocessing unit; the law and regulation graph construction iteration unit is used for extracting a'law and regulation term number-verification dimension-constraint condition 'triple of the law and regulation in the dangerous cargo boxing field and introducing an improved three-dimensional dynamic weight conflict resolution algorithm according to the efficacy level, the release subject and the application range indexing; a multi-agent collaborative verification unit; a risk early warning report generation unit; and a man-machine cooperation closed loop unit. According to the method, the problems of lagging of laws and regulations and clause conflicts are solved by adopting triple extraction of'laws and regulations clause numbers-verification dimensions-constraint conditions', a three-dimensional dynamic weight conflict resolution algorithm based on laws and regulations effectiveness-scene matching degree-release time and a technical means of periodically optimizing weight parameters through a reinforcement learning model.

The invention discloses a method for monitoring and identifying dangerous behaviors of a smart park, and relates to the field of intelligent security and protection. The method comprises the following steps: acquiring an environment data stream through a sensing device deployed at a key point location of a park and carrying out standardization processing on the environment data stream; the data stream is sent to an edge computing node, a target is recognized through a lightweight target detection model, the detection confidence coefficient is obtained, a motion track is generated through a multi-target tracking algorithm, the rule matching degree is calculated according to a predefined rule base, and dangerous behaviors are preliminarily recognized; calculating a comprehensive model prediction credibility based on the detection credibility and the rule matching degree, and generating a final comprehensive credibility through a multi-dimensional credibility fusion model in combination with the time sequence consistency credibility and the scene matching credibility; and dangerous behavior trend prediction is carried out according to the comprehensive confidence, and an alarm instruction is generated to trigger disposal linkage, so that advanced prevention and quick response of dangerous behaviors are realized, and the intelligent level and operation and maintenance efficiency of park security and protection are greatly improved.

The invention provides a business travel data management method and system based on cloud computing and scene matching and computing equipment. The method comprises the following steps: acquiring at least one geographic image uploaded by a user in a business travel plan execution process; generating a compliance geographic region set according to the travel plan; matching the geographic image with the compliance geographic region set to obtain a first matching result; identifying and extracting visual scene features in the geographic image, and performing consistency cross validation comparison on the visual scene features and a geographic scene expected based on geographic coordinates to obtain a first comparison result; generating an authenticity verification result of the travel behavior based on the first matching result and the first comparison result; and when the authenticity verification result is that the verification is passed, automatically generating an electronic audit voucher containing a spatial trajectory chain and a geographic image. According to the technical scheme of the invention, the accuracy of authenticity verification of travel behaviors can be improved, a high-credibility electronic audit voucher is automatically generated, and a data closed loop is provided for intelligent travel management.

The invention relates to the technical field of information extraction, and discloses a long text news event element extraction method and system based on multi-agent collaboration, and the method comprises the following steps: constructing a large language model multi-agent system architecture; the central coordination agent receives the unstructured news text; the text segmentation agent outputs a logic unit and time description thereof; an event extraction agent extracts an event subject, an action and an object, and complements time and entity role information; the scene matching agent judges the fine-grained news scene category to which the scene matching agent belongs; the information extraction agent generates a structured event record; and the central coordination agent performs quality auditing and exception handling on each step of output to ensure the integrity and consistency of the output data. According to the method, the static relationship between the event main bodies can be constructed according to the structured event information through the relationship construction and context sorting agent, the event situation change is summarized based on the timeline, and the feasibility and the visualization of news event analysis of the system are improved.

The invention belongs to the technical field of commercial entertainment recommendation, and discloses a matching method for recommending online and offline script playing methods based on an AI algorithm, and the method comprises the steps: collecting user immersion experience data, carrying out the multi-dimensional deconstruction of the immersion experience data, and obtaining a user preference pedigree; performing script emotion tension and interaction density analysis on the script content to form a cross-scene experience map; designing a dual-channel sensing network, and performing cross fusion on the user preference pedigree and the cross-scene experience atlas based on the dual-channel sensing network to generate a personalized matching matrix; establishing an immersion degree evaluation parameter set, and performing scene adaptability calculation on the personalized matching matrix to obtain a scene conversion coordination degree index; generating a script recommendation sequence according to the scene conversion coordination degree index, and realizing real-time recommendation adjustment and experience feedback collection through context awareness; the difference between online and offline script experiences is effectively quantified, and a scientific cross-scene matching basis is provided.

The invention discloses a game graphic design automatic scene matching method and system based on deep learning, and relates to the technical field of image matching, and the method comprises the steps: obtaining a background base map and a plurality of plane element image libraries of a game scene; performing image segmentation on the background base image, dividing the background base image into a plurality of to-be-matched areas, and extracting an image feature vector of each to-be-matched area; establishing a semantic tag generation model, and generating a scene semantic tag of each to-be-matched area; according to the scene semantic tag, screening a plane element matched with each to-be-matched area from a plane element image library, and calculating a matching degree score of the plane element and the to-be-matched area; and placing the plane element with the highest matching degree score in the corresponding to-be-matched area to generate a complete game scene design drawing. The complete and coordinated game scene design drawing can be automatically generated, selection and layout of game materials are optimized, and design efficiency and effect consistency are improved.

The invention discloses an agricultural scene recognition and verification method and system based on multi-modal data, and belongs to the technical field of agricultural scene recognition. Multi-dimensional agricultural scene data such as images, weather and soil are collected through multiple sensors, and a standardized data set is formed through preprocessing such as data cleaning, calibration and space-time registration; using a soft voting integrated classifier to carry out agricultural scene identification on the image and time sequence features extracted from the standardized data set; based on the recognition result, the scene analysis model management library combines the scene matching degree and system resource dynamic selection, adjusts a scene analysis model and parameters thereof, and then completes multi-modal analysis to obtain a multi-modal data set containing rich information; and finally, carrying out multi-dimensional quality evaluation on the multi-modal data set. According to the method and the system, multi-modal data is effectively fused, the agricultural scene recognition accuracy is improved, computing resources are reasonably allocated, comprehensive and reliable data support is provided for agricultural production decisions, and the adaptability of the system to complex agricultural scenes is enhanced.

The invention relates to the technical field of image processing, in particular to an in-orbit multi-stage real-time automatic dimming device and method for a spaceflight area-array camera. According to the device, an imaging module obtains an original scene image according to set integral time and transmits image data to an image coarse adjustment module; the image coarse adjustment module counts gray level distribution of an original image in real time, calculates first updating integral time, packages image data without overexposure or underexposure and sends the packaged image data to the image fine adjustment module; after the image fine adjustment module receives the image, scene recognition and gray level evaluation are carried out through a deep learning model, exposure parameters are dynamically adjusted according to the scene type, and image exposure control is achieved; and the image coarse adjustment module and the image fine adjustment module form a two-stage closed loop, so that the imaging module obtains integral time matched with a scene in real time. The method has the advantages that the integral time of the detector is automatically adjusted according to the detail content of the target scene, and the target image with the best image impression and detail is captured.

The invention relates to the technical field of twin power station simulation, and discloses a twin power station collision visual simulation method based on physical simulation. The method comprises the following steps: acquiring real-time physical parameters and an operation state of the twin power station, and establishing a dynamic simulation model fitting the reality according to the real-time physical parameters and the operation state; analyzing a historical collision simulation record, dividing a historical strategy into multiple cognitive levels in combination with a deviation degree between a simulation result and an expected target and a simulation strategy reliability index, and constructing a structured multilayer knowledge base; and for a preset target collision scene in the model, generating an adaptive simulation strategy of each stage, and screening an adaptive historical strategy from the knowledge base according to the reliability and the scene matching degree. The two strategies are fused and adjusted through a coordination mechanism to adapt to real-time changes of the power station, a control instruction is generated after an optimized simulation strategy is determined, collision simulation execution is driven, and finally a visual result is output and an actual simulation state is obtained. According to the method, the accuracy and adaptability of collision simulation can be improved.

The invention discloses a building light environment neural network control system based on BIM, relates to the technical field of building intelligent control, and is used for solving the problem of poor intelligent illumination adaptive control. The method comprises the following steps: constructing a structure enhanced semantic map based on a BIM model, extracting a spatial structure attribute and a communication relationship to generate a node vector and an edge weight, and describing an illumination propagation relationship; the method comprises the following steps: constructing an original state vector by fusing sensing data and structural information, carrying out time sequence modeling by adopting a gated recurrent neural network, dividing four types of subspaces to express environment evolution characteristics, identifying a lighting task scene according to a current activity and time period, matching a control target, reasoning an adjustment direction, and screening a candidate action path; according to the method, the optimal strategy is generated and executed, user feedback is collected to construct behavior response factors, user preferences are updated, a preference closed-loop mechanism is constructed, illumination control cooperatively driven by structure perception, state evolution and user preferences is achieved, and the comfort, intelligence and energy saving performance of the building light environment are improved.

The invention relates to the technical field of service quality evaluation, in particular to a multi-dimensional after-sales service evaluation scoring system, which comprises a responsibility description comparison module, a node process propulsion module, a scoring dimension generation module, a scoring data acquisition module and a responsibility attribution judgment module. According to the invention, through the description and comparison of responsible persons of audio and text dual channels, the verification of node sequence consistency and conclusion content consistency, the linkage judgment of the fusion task state and personnel identity, and the linkage circulation logic of on-site sign-in data, and the dynamic mapping of the scoring dimension in combination with multivariate parameters such as the service type and the emergency degree. Scoring data are collected in a layered mode, score differences and time sequences are fused, responsibility traceability labels are established, multi-dimensional data synchronous verification, flow node collaborative propulsion, dynamic scoring scene matching and responsibility affiliation transparent management are achieved, the dispute recognition capacity in a complex service scene is enhanced, and the responsibility traceability is improved. And the adaptability of the whole-process tracing fineness of the work order and the service link evaluation is improved.

The invention provides a non-abandoned collection model generation method and system based on a GAN style constraint mechanism, and relates to the technical field of digital protection and display of intangible cultural heritage. According to the method, the generative adversarial network is driven by integrating the user portrait data and the non-missing basic data, the artistic style is accurately controlled in combination with a style constraint mechanism, dynamic scene matching display is realized by utilizing an augmented reality technology, and the method has the advantages of improving non-missing digital modeling efficiency, enhancing user interaction experience and guaranteeing culture inheritance accuracy.

The invention discloses a laser SLAM loopback correction method based on scene matching, and belongs to the technical field of synchronous positioning and mapping of robots, and the method comprises the following steps: S1, extracting a line feature, a surface feature and a point feature of a current key frame; s2, constructing a point feature descriptor and a line feature descriptor of the current key frame; s3, screening out a plurality of loopback candidate frames from the historical key frames by using the hash values; s4, screening loopback frames from the plurality of loopback candidate frames; if the loopback frame is screened out, skipping to S5; if the loopback frame is not screened out, skipping to S7; s5, optimizing a pose map based on the relative pose of the current key frame and the loopback frame, and correcting a historical track and a historical map of the robot based on the optimized pose map; s6, reloading the historical key frame according to the corrected historical track, and skipping to S1; and S7, not starting pose map optimization, and skipping to S1. The method can comprehensively describe the global characteristics of the point cloud, and improves the loopback detection recall rate.