355 results about "Global consistency" patented technology

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses a personalized federated learning optimization method and system, and mainly solves the problem of poor performance of an existing personalized federated learning model. The method comprises the following steps: establishing a communication link between a client and a server; each client receives a current global sharing model parameter broadcasted by the server, loads the current global sharing model parameter to a local model, and introduces a total loss function of a dynamic alignment strength definition model; training and optimizing local model parameters, and updating shared parameters by using the parameters; the client side calculates a self-adaptive aggregation weight based on the parameter updating quantity norm, the data volume weight and the synchronous frequency weight of the client side, and uploads the self-adaptive aggregation weight and the updated shared parameters to the server; and the server receives the parameter and weight information uploaded by the server, executes global model aggregation to obtain an updated global model, and outputs the global model reaching accuracy convergence or a preset training round on the verification set. According to the method, local personalization and global consistency can be balanced, the robustness and efficiency of global aggregation are improved, and the method can be used for processing scenes of high data isomerism and dynamic change of client participation states.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly discloses a multi-view three-dimensional Gaussian densification method and system for adaptive density control, and the method comprises the following steps: collecting a multi-view original image, and carrying out the preprocessing of the multi-view original image; complexity features are extracted, a pixel-level complexity heat map is generated, and a globally unified three-dimensional complexity field is constructed; performing back projection on the reconstruction residual error, high-frequency inconsistency and depth / geometric consistency cost of each view angle, generating three-dimensional error popularity, determining a candidate newly-added set and a candidate pruned set, generating a weak label to train a lightweight multilayer perceptron classifier, outputting a ternary probability corresponding to newly-added / pruned / maintained, and obtaining a new / pruned / maintained three-dimensional perceptron classifier; and performing Gaussian densification operation on the newly added region. By adopting the technical scheme, fine point adding is carried out on the complex area, effective pruning is carried out on the simple area, and meanwhile, the synthesis quality, the global consistency and the calculation efficiency of the new view angle are improved.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention discloses a heterogeneous computing system, a cache consistency maintenance method and device, equipment and a medium, and relates to the technical field of heterogeneous computing. The method comprises the steps that when a data operation request is detected, if a receiving party and a transmitting party are a cluster cache layer and a global cache layer, the protocol format of the data operation request is converted according to the mapping relation between a group consistency protocol and a global consistency protocol. And when the data is forwarded to the local cache layer, executing corresponding operation based on the memory consistency model of the calculation unit to which the data belongs, and maintaining the consistency of the local cache. And when the data is forwarded to a cluster cache layer, corresponding operation is executed based on the cluster consistency protocol, and the consistency of the cluster shared cache is maintained. And when the data is forwarded to a global cache layer, maintaining the consistency of the shared caches of the clusters based on a global consistency protocol. The method and the device can solve the problem of poor compatibility and expansibility of related technologies, can be compatible with various computing units and different memory consistency models thereof, and are easy to expand.

The invention relates to the technical field of medical data processing, and discloses a medical data source real-time processing and quality control method based on distributed computing. The method comprises the steps of obtaining and classifying multi-source heterogeneous medical data streams, constructing a distributed parallel processing framework, setting an initial constraint condition, executing distributed streaming computation to generate a primary processing strategy, and optimizing and generating a global consistency processing strategy by using a federated learning algorithm. The system comprises a plurality of modules such as a data access and classification module and a processing framework construction module. According to the method, multi-source heterogeneous medical data can be efficiently processed, real-time calculation is realized through a multi-dimensional processing space and dynamic task allocation, and the data quality and privacy are guaranteed by utilizing a federal learning optimization strategy. And equipment abnormity can be monitored, quality risks can be predicted, task scheduling can be managed, the accuracy, reliability and availability of medical data processing can be improved, and powerful support can be provided for medical decision making and the like.

The invention discloses an intelligent segmentation method and device for ceramic matrix composite CT image defects, and belongs to the technical field of image detection. The method comprises the following steps: inputting preprocessed CT image data into a preset double-branch collaborative segmentation model, and respectively outputting to obtain a local texture feature map and a global relation feature map; performing three-branch material perception fusion processing on the local texture feature map and the global relation feature map according to a preset perception fusion model to obtain a fusion feature map containing local detail accuracy and global consistency; sequentially carrying out progressive decoding and refined reconstruction processing on the fused feature graph to obtain a defect probability graph; and calculating a loss function of the defect probability graph and a manually pre-labeled real defect segmentation graph, and dynamically adjusting the loss weight of the initial model according to a calculation result to obtain a CT image defect segmentation model meeting a preset convergence condition. The method can meet the detection requirements of industrial scenes on the defects of the ceramic-based composite material.

The invention relates to the technical field of video analysis, in particular to a video editing method based on grid layout alternate diffusion and multi-attention control, and the method comprises the steps: segmenting an original video frame sequence into a plurality of grids, each grid comprising a plurality of pixel space video frames which are continuously arranged, and forming grid data; mapping the gridding data to a low-dimensional submerged space through an encoder, and generating initial submerged space feature data; the initial submerged space feature data are edited, the editing process comprises a diffusion process and a sampling process, the diffusion process is based on a pre-trained stable diffusion model, and a time attention module is embedded in the diffusion process; in the sampling process, executing an odd-even time step alternate replacement strategy on the grid layout to promote cross-grid global consistency, and dynamically fusing attention maps of a reconstruction branch and an editing branch according to a timestamp threshold to generate de-noised data; and decoding, splitting and recombining the de-noised data through a decoder to generate an edited continuous video frame sequence.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.

The invention discloses an autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention provides a multi-granularity open vocabulary query method based on an object-level lossless Gaussian field, and the method comprises the steps: introducing an object-level Gaussian field with a global consistency codebook, rendering a learnable semantic tag vector in the Gaussian field back to a corresponding object tag; the direct mapping between the label and the corresponding uncompressed high-dimensional feature is established through the code book, so that the semantic feature of any dimension is supported, additional compression is not needed, and the understanding capability on an object is remarkably improved; according to the method, wide quantitative and qualitative evaluation is carried out in a plurality of scenes, excellent performance in the aspects of object level zero sample segmentation and open vocabulary understanding is shown, the highest precision is particularly achieved in object-component hierarchical retrieval, and meanwhile multi-granularity scene editing is supported.

The invention provides a burn wound area segmentation method and system, and the method comprises the steps: obtaining a to-be-segmented image through constructing an anatomy database, and employing a lightweight CNN classifier to extract a feature vector; determining a healing stage of the burn wound according to the feature vector, and adjusting boundary parameters according to the healing stage; inputting an image to be segmented into the improved U-Net encoder network, and outputting a segmentation mask and a boundary probability graph; determining an interaction area according to the boundary probability graph, carrying out interaction in the interaction area by referring to the segmentation mask, and generating a smooth boundary according to interaction data; and post-processing optimization fused with prior constraints is performed on the smooth boundary to obtain a target boundary so as to complete burn wound area segmentation, specific and prior knowledge provides basic constraints for segmentation, interactive operation pertinently corrects a complex area, post-processing realizes global consistency optimization, and a'constraint-correction-optimization 'closed-loop processing flow is formed.

The invention discloses an underground pipeline three-dimensional deformation quantitative inspection method, which comprises the following steps: processing a multi-source sensing sequence to generate a baseline data packet; the method comprises the following steps of: performing first-stage on-line correction, acquiring a medium state agent quantity by applying micro-excitation sampling combined by a micro-amplitude angle and double power to laser, and performing joint inversion to obtain section geometric data subjected to medium correction by utilizing a forward model with endogenous refraction and scattering correction items; second-stage scale robustness processing is carried out, in a unified optimization framework, motor current-based dynamic consistency constraint, geometric conservation constraint and wellhead external reference strong constraint are fused, and a globally consistent pose track is solved; and performing three-dimensional reconstruction and deformation quantification according to the corrected section data and the pose track. According to the invention, the accuracy of geometric measurement of the pipeline and the global consistency of the pose track in a complex environment such as a humid environment can be improved.

The invention discloses a personalized federated learning method based on a kernel distance between clients, a framework and an application, and belongs to federated learning and an application technology thereof. The method aims at double challenges of data isomerism and privacy protection, and is based on a federated learning framework of a conditional policy network and differential privacy. The CPN adaptively balances the conflict between the global consistency and the local personalized characteristics by dynamically generating the weights of the personalized characteristics and the global characteristics, so that the influence caused by data isomerism is effectively relieved. Meanwhile, the differential privacy technology protects the sensitive data characteristics of the client and reduces the risk of privacy disclosure by injecting Gaussian noise in gradient updating. The personalized federal learning method provided by the invention not only is superior to the existing mainstream method in global model performance and fairness between clients, but also realizes good balance between privacy protection and model performance.

The invention discloses a method and system for constructing a city information model, and belongs to the field of city intelligent management, and the method comprises the steps: carrying out the spatial registration and semantic alignment of future multi-source heterogeneous data through a unified coordinate reference, and constructing a multi-dimensional incidence relation between a spatial entity and the attribute of the spatial entity through a graph database; performing time serialization processing on the city information model based on the real-time sensing data, and performing real-time correction on the geometric state and the attribute of the model by adopting an incremental modeling algorithm; performing automatic calibration on the model data in combination with rule reasoning and probability correction methods; predictive calculation is carried out on multiple scenes, and parameterized optimization is carried out on the city information model according to a calculation result; and carrying out adaptive hierarchical abstraction on the city information model, and automatically generating model subsets with different precision levels. According to the method, spatial registration and semantic integration are performed on the data, so that the global consistency of the city information model is realized, and the problems of data inconsistency, repetition and redundancy in a traditional method are solved.

The invention discloses a real scenic spot cloud data and BIM point cloud data fusion method and device, and belongs to the technical field of point cloud registration, and the method comprises the steps: obtaining real scenic spot cloud data of a railway scene; building a building information model BIM of the railway scene; based on the BIM of the railway scene, acquiring BIM point cloud data of the railway scene; performing multiple registration on the BIM point cloud data and the real spot cloud data; converting the registered BIM point cloud data and the real scene point cloud data into a global coordinate system to obtain global point cloud data; and correcting the global point cloud data by taking the geometric consistency function and the global consistency function as constraints to obtain a fusion result of the real scenic spot cloud data and the BIM point cloud data. According to the method, high-precision fusion of the real spot cloud and the BIM point cloud can be realized.

The invention provides a three-dimensional point cloud reconstruction method and application thereof in target detection. The point cloud reconstruction method comprises the following steps: acquiring an incomplete original point cloud; the original point cloud is input into a trained point cloud completion model, a complete point cloud is obtained, and the point cloud completion model comprises a feature extraction module, a feature generation module, an encoder, a query generator, a decoder and a reconstruction head. According to the method, automatic completion of incomplete point clouds is realized by constructing a point cloud completion model, global consistency and local detail expression are considered during feature extraction and generation, and the multi-scale representation capability and generalization performance of the point clouds are remarkably enhanced; the feature of the prediction center point is quickly generated through an encoder-decoder structure, the missing point cloud is predicted based on the reconstruction head, all the point clouds are fused, and the point clouds can be complemented and predicted more accurately through the method.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI-based snowfield trajectory and extreme environment intelligent identification method, which comprises the following steps: a multi-modal data acquisition module; a fusion type deep learning model; a confrontation generation data enhancement unit; an adaptive control subsystem; and an edge calculation node deploys a lightweight model inference engine to realize low-delay trajectory feature extraction and classification decision. According to the method, a multi-scale feature pyramid structure and a cross-modal attention mechanism are adopted, the convolutional neural network and the recurrent neural network are combined, and spatial features and modeling time sequence dynamics can be extracted at the same time. The synthetic data of the extreme snowfield scene is generated by using the conditional generative adversarial network, and the physical constraint generator and the multi-scale discriminator are combined, so that the real snow particle motion trail can be simulated, and the global consistency and the local texture authenticity of the generated data are ensured.

The invention discloses an image virtual dyeing system based on unsupervised learning and combining a diffusion process and an autoregression model, which comprises a data preprocessing module, a random sequence mask autoregression model, a diffusion loss denoising module, a pathological consistency constraint module and an image generation module, a plurality of pixel areas of a target image are predicted by using a random sequence, pixel position dependency in an image generation process is avoided, capture of a long-range dependency relationship is enhanced, global and local features are effectively captured, local information loss is avoided, global consistency and fidelity of local details are improved, and the image quality is improved. And the quality of the image is gradually optimized through a progressive denoising process in the diffusion loss denoising module, so that the finally generated IHC image is more real, the condition distribution of each image pixel is simulated, a more continuous and smoother IHC dyed image is generated, and the problem of noise and detail loss in the conversion process is avoided.

The invention discloses a cache maintenance system of a heterogeneous computing system and electronic equipment, and relates to the technical field of data processing, a first directory controller manages the consistency state of a host end, and a second directory controller manages the consistency state of an equipment end. The consistency maintenance operation is firstly efficiently processed by the corresponding directory controller in the storage domain, cross-device frequent coordination communication is reduced, and when cross-domain data access occurs, the two directory controllers cooperate through an established communication coupling mechanism and execute global consistency maintenance as required. The method not only can adapt to a high-burst and high-parallel access mode of a device end, but also can avoid direct conflicts with a cache management mechanism of a host end, and realizes global consistency through distributed collaboration. The technical problem of performance bottleneck of cache consistency in the heterogeneous system is solved, and the technical effect that the overall data access efficiency of the heterogeneous system is improved while the correctness is maintained is achieved.

The invention discloses a distributed database data synchronization system and a data consistency detection method. The method comprises the following steps: generating a data version sequence set through multiple data synchronization tests, extracting a node independent version sequence, calculating a version change range to judge node synchronization consistency, sequencing the synchronization consistent nodes to calculate cluster overall consistency, positioning and isolating abnormal nodes, and calibrating cross-cluster consistency under a multi-cluster architecture. And finally generating a global consistency report. The system is composed of a data synchronization management module, a consistency detection module and the like. The two embodiments respectively display the application processes of a single-cluster e-commerce system and a multi-cluster financial system, and the scheme realizes efficient data consistency detection, automatic exception handling and cross-cluster management, and improves the system reliability and operation and maintenance efficiency.

The invention discloses a power distribution network line fault positioning method, device and equipment, and relates to the technical field of power distribution network line fault positioning, and the method comprises the steps: obtaining operation data and lightning ground lightning data; feature extraction is carried out on the operation data, spatial-temporal feature mapping is carried out on the lightning and ground lightning data, and multi-source feature parameters are generated; probabilistic reasoning is carried out on the input multi-source characteristic parameters through section agents deployed in a distributed mode and Bayesian network submodules of the section agents, and real-time fault probability values of all independent sections of the power distribution network are output; and taking the real-time fault probability value of each independent section as an input, dynamically coordinating an output result of each agent through a Nash equilibrium strategy, and generating a global consistency fault positioning decision. The method is used for solving the problem that a traditional line fault positioning method is difficult to effectively fuse heterogeneous data and is difficult to coordinate local judgment conflicts.

The invention provides an Internet of Vehicles data sharing and storage model based on a double-layer block chain, and aims to solve the problems of low data interaction efficiency, poor expansibility, insufficient security and the like in the traditional Internet of Vehicles. According to the model, road side units are grouped geographically, an optimized Raft protocol is adopted in each group to realize rapid consensus, and an improved PBFT protocol is adopted among the groups to ensure global consistency. Self-adaptive leader election, batch processing and assembly line mechanisms are introduced into the Raft protocol, so that the throughput and response speed of the system are improved; a reputation value-based VRF main node election mechanism and a BLS aggregation signature technology are introduced into the PBFT protocol, so that the communication complexity is reduced, and the system security and fault-tolerant capability are enhanced. According to the method, the fault-tolerant rate and the message complexity of the system are analyzed theoretically, the advantages of the system in the aspects of throughput, time delay and fault-tolerant performance are verified through simulation experiments, and the method is suitable for large-scale and high-concurrency car networking application scenes.

The invention provides a micro-grid group energy interaction optimization method based on ADMM, and belongs to the technical field of micro-grid groups, and the method comprises the steps: firstly constructing a double-layer energy management architecture, and achieving the hierarchical management of macroscopic coordination and microcosmic regulation; establishing a multi-objective function, and optimizing the operation cost in combination with a self-adaptive gating balance function; then, considering various constraint conditions to establish an optimization model; an ADMM algorithm is adopted to decompose a global problem into parallel sub-problems, and global consistency is coordinated through interaction of expected power values; carrying out error modeling prediction on the energy output and load demand of each micro-grid; energy interaction benefits are evaluated in real time based on a deep energy management neural network, and interaction priorities are dynamically adjusted; and finally, judging iteration termination according to a residual convergence condition, and outputting an optimal scheduling strategy, thereby solving the technical problem of low collaborative optimization scheduling efficiency of the micro-grid group under a high-proportion renewable energy source grid-connected condition.

The embodiment of the invention provides a relation graph reconstruction method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring an initial relation graph, and generating a node feature matrix and an initial adjacency matrix corresponding to a plurality of target nodes based on the initial relation graph; performing feature transfer and feature aggregation among a plurality of target nodes through the target model to obtain an intermediate feature of each target node; mapping the intermediate feature to a potential space to obtain a distribution parameter; performing random sampling on the Gaussian distribution to obtain a potential representation vector of each target node, and generating a potential representation matrix based on a plurality of potential representation vectors; predicting a connection probability of any two target nodes based on the potential representation matrix to obtain a target adjacency matrix; and performing relation reconstruction on the initial relation graph according to the node feature matrix and the target adjacent matrix through the target model to obtain a target relation graph. Therefore, the global consistency and accuracy of the reconstructed relation graph can be improved.

The invention discloses a dynamic sparse cross-modal fusion data feature extraction method and system, and the method comprises the steps: multi-modal feature coding and alignment, and dynamic sparse cross-modal fusion, which are cooperatively completed by a near-end operator sparse controller and a Top-k sparse cross-modal attention module. After dynamic sparse fusion, features containing highly concentrated cross-modal information are obtained, and through a hierarchical data encoder module, the features of the cross-modal information are converted into data representation with a compact structure and rich semantics; a hybrid expert architecture is adopted; the system comprises a modal feature encoder, a dynamic sparse fusion layer, a data encoder and a task adapter. According to the invention, the calculation complexity of traditional cross-modal fusion is greatly reduced; the data has global consistency while keeping fine-grained details; and meanwhile, the richness of cross-modal semantics is kept, and the method is suitable for feature extraction tasks of multi-modal data such as images, audios and the like.

The invention provides a high-precision 3D virtual portrait reconstruction method for a single two-dimensional image, and the method comprises the following specific steps: S1, super-resolution preprocessing: carrying out the preprocessing of an input low-resolution human body image through a deep learning super-resolution network, S2, multi-view diffusion generation, combining an SMPL-X human body prior model and view coding, and carrying out the reconstruction of a single two-dimensional image. A multi-view image is generated by using a conditional denoising diffusion model, and local and global consistency is ensured through a 1D-3D mixed attention mechanism. And S3, a geometric enhancement double-branch module: generating an RGB image and a normal graph in parallel, and providing texture and geometric double constraints for reconstruction. And S4, multi-scale iterative optimization: feeding back the multi-view image to an SMPL-X parameter estimation module, and gradually correcting attitude and shape parameters through gradient optimization. Through a multi-module cooperation and iterative optimization mechanism, the problems of texture detail missing, geometric inconsistency, parameter errors and multi-view generation are solved, and an efficient solution is provided for applications such as virtual reality and digital humans.

The invention provides an anti-cheating intelligent weighing system and method based on a multi-source signal, and relates to the technical field of weighing, and the method comprises the steps: carrying out the dynamic calculation based on pressure distribution time sequence data, an object three-dimensional contour, a gravity center position track and deformation displacement field data which are synchronously collected in real time, and combining a preset conduction reference library to generate multi-source features; dynamically calculating and generating a first weight, a second weight and a third weight based on a force conduction deviation value, a contour confidence value and a pressure distribution entropy value in the multi-source features; dynamically calculating and generating a dynamic credible threshold based on the first weight, the second weight and the third weight in combination with the geometric cross-border proportion value and the force conduction deviation degree value; calculating a global consistency index in the current weighing period based on the conduction reference library; based on the global consistency index and the dynamic credible threshold, an anti-cheating result is generated according to a preset judgment condition, and high-precision and high-reliability weighing anti-cheating is achieved through multi-source signal fusion and dynamic analysis.

The invention discloses a standard courseware generation method for an artificial intelligence learning mode, and relates to the technical field of educational informationization, and the method comprises the steps: firstly collecting a course standard, a world language ready framework and translation memory, and generating a reference semantic vector set with a unique version fingerprint through vectorization; mapping the multilingual courseware draft on the multi-lingual courseware draft, outputting a weighted difference vector and recording a timestamp; the difference vector is decomposed into three factors of emotional tendency, cultural symbols and term consistency, and a deviation registration form is generated according to a dynamic threshold value; generating type rewriting, template replacement or manual prompt is selected according to a rewriting energy function, the high-risk chunks are locally revised, and consistency marks containing version fingerprints are written in; and finally, carrying out global residual error recheck, generating a consistency report after confirming the quality through a global consistency index, packaging the consistency report and the revised courseware into a release package for multi-end synchronization, writing fingerprints on a chain, and constructing a whole-course reliable new cross-language courseware quality closed loop.

The invention relates to a laser point cloud mapping and matching method based on a cross-country scene, and the method comprises the steps: 1, collecting a cross-country environment point cloud, carrying out the filtering of the point cloud, removing a dynamic object point cloud, and forming real-time point cloud data; step 2, performing pose prediction estimation on the vehicle sensor data through Kalman filtering, and correcting the predicted pose to obtain final pose estimation; 3, adding real-time point cloud data with semantic tags to the local increment dense voxel semantic map through final pose estimation to form a local increment dense semantic map; 4, performing point cloud registration on the real-time point cloud data and the local increment dense semantic map through a semantic point cloud observation model, and obtaining a point cloud key frame according to point cloud registration and final pose estimation; and step 5, estimating the accurate pose of the vehicle according to the point cloud key frame and the pose information corresponding to the point cloud key frame, constructing a consistency map, realizing global consistency optimization of the map, and completing construction of a global semantic map.