275 results about "Global consistency" patented technology

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 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 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 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 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 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 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 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 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 the technical field of medicine management, and discloses a medicine informatization whole-process management system based on big data analysis. According to the system, a drug full-life-cycle multi-dimensional data stream is captured through a distributed sensing node network, and a drug state tensor is generated after noise filtering and missing value compensation are carried out through a real-time stream processing engine. The risk identification module scans and locates an abnormal data cluster through a multi-scale mode and constructs a drug quality hypersurface model; and the data aggregation module establishes a mapping relation between a state tensor and a quality model by using a space-time diagram attention network and calculates a global consistency metric value. The intelligent decision-making module generates an optimal drug management action sequence based on the measurement value under multiple constraint conditions, the closed-loop control module executes the action sequence and monitors system state deviation in real time, data acquisition parameters are dynamically calibrated, and autonomous optimization and closed-loop management of the whole process of drugs are achieved.

According to the task cooperative processing method and device applying the distributed agent network and the medium, the task processing request sent by the user terminal is received, hierarchical semantic analysis and task dependency relationship analysis are carried out to generate the structured atomic task set, the structured atomic task set is input into the distributed agent network, and the task cooperative processing efficiency is improved. And carrying out collaborative decision making and execution intention confirmation on the atomic task units, constructing a dynamic task graph according to a decision making result and a dependency relationship descriptor, and executing the corresponding atomic task units in parallel according to a task distribution relationship in the dynamic task graph. The method comprises the following steps: exchanging task execution state information and intermediate data output in real time through a preset publishing and subscribing communication mode, generating a distributed execution track based on an exchange result, carrying out association fusion processing on execution results of atomic task units, calculating a global consistency weight, carrying out weighted aggregation on the execution results based on the global consistency weight, and generating a distributed execution track; and generating a global task processing result and sending to the user terminal. According to the invention, the task cooperative processing accuracy can be improved.

According to the clearance robot indoor and outdoor fusion navigation method and system, closed-loop detection and error state Kalman filtering are introduced, a feedback mechanism is established, accumulative errors of long-term operation are corrected in a bidirectional mode, and the global navigation precision is ensured. By constructing a multi-level semantic map and fusing laser, visual and inertial data in a tight coupling mode, high-precision and robust positioning and navigation of the cleaning robot in a complex community environment are realized. And on the basis of the closed-loop constraint in the step A5, the system can automatically adjust the prediction time domain and the control time domain of the model prediction controller, so that a smoother, more energy-saving, more stable and safer walking path can be planned. And after real-time pose correction of the filter, a robot pose sequence with higher global consistency is obtained and serves as input of the closed-loop detection module, so that subsequent global pose optimization is more effective and accurate.

The invention discloses a text-to-image pedestrian re-identification method based on dynamic memory enhancement, and relates to the technical field of computer vision. A dynamic memory enhancement module is designed, a prototype memory library capable of being iteratively updated is constructed based on a modern Hupfield network, multi-modal co-occurrence attribute features are dynamically stored and retrieved, sample specific noise is effectively inhibited, and a discriminative cross-modal covariance mode is reserved. Text-to-image pedestrian re-identification is reconstructed into a global-local collaborative alignment task, through an attribute fine-grained alignment module, a corresponding relation between a visual area and text features is established by utilizing single-instance contrast learning, and local fine-grained semantic alignment is realized through adaptive feature subspace reconstruction without damaging global consistency. According to the method, multi-loss function optimization strategies such as InfoLOOB loss and SDM loss are fused, the stability and discrimination of cross-modal matching are enhanced, the core problems of large intra-class variation and high inter-class similarity are effectively relieved, and more accurate and efficient text-to-image pedestrian re-identification is realized.

The invention discloses a remote sensing image segmentation method based on frequency domain global channel perception and cross-channel attention fusion, and relates to the technical field of remote sensing image processing. Comprising the steps of inputting remote sensing image features, mapping the remote sensing image features to a frequency domain, performing amplitude enhancement and phase channel transformation, reconstructing frequency domain features, and inversely transforming the frequency domain features back to a spatial domain to obtain frequency domain enhancement features; a cross-channel attention mechanism is applied to the frequency domain enhanced feature, a channel dependency relationship is modeled through query, key and value interaction, and the frequency domain enhanced feature is fused to obtain a fused feature; and converting the fusion feature into a segmentation image, and performing model optimization through a multi-task loss function. According to the method, the boundary fineness and small target detection can be considered while the global consistency is ensured, meanwhile, the calculation complexity is reduced, and the practicability and the engineering feasibility of the method are improved.

The invention discloses a self-supervised tomographic SAR (Synthetic Aperture Radar) reconstruction method based on orthogonal subspace decomposition. According to the method, a traditional single original space is reconstructed and expanded into a Range-Null-Raw triple space collaborative learning framework, an R2R and N2N principle-based self-supervised loss function is constructed by using an orthogonal subspace decomposition characteristic of a linear measurement operator, direct mapping learning from noise measurement data to a high-quality reconstruction result is realized, and the real-time performance of a real-time reconstruction system is greatly improved, so that the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance is improved. Observable components are learned through Range space, unobservable components are learned through Null space, a cross constraint strategy of global consistency is ensured through Raw space, and the adaptability of the model to real measurement conditions is enhanced. The performance gap between simulation training and actual deployment in deep learning TomoSAR reconstruction is effectively solved, and a new solution is provided for reliable application of an unsupervised deep reconstruction network in a real TomoSAR imaging task.

The invention relates to the technical field of point cloud data processing, and provides a ground filtering method and system based on cloth simulation parameter dynamic optimization, and the method comprises the following steps: obtaining point cloud data of a to-be-processed region, carrying out the preprocessing, constructing a complexity index, and calculating a complexity index value based on the preprocessed data; clustering the obtained complexity indexes by adopting a clustering method fusing an elbow rule and a spatial neighborhood constraint to obtain a terrain complexity category; and selecting distribution parameters of a distribution algorithm based on the terrain complexity category and the dynamic mapping of the constructed complexity category-optimal parameter group, and further performing ground point filtering and ground extraction by adopting the distribution algorithm. According to the invention, distribution simulation parameter configuration is guided through terrain complexity analysis, accurate adaptation of different terrain areas is realized, and the accuracy and global consistency of ground point extraction are improved.

The invention discloses a large model illusion detection and inhibition method and system based on logic type guidance, and belongs to the technical field of large model illusion detection, and the method comprises the following steps: logic diagram matching, multi-hop path screening, consistency verification, semantic reconstruction correction and text verification output. According to the method, a text reasoning logic diagram is constructed, a multi-hop reasoning relation, causal constraints and a logic sequence which are implied in a natural language are subjected to explicit structured representation, and a knowledge graph is introduced to perform logic consistency verification. On the basis, based on logic type constrained multi-hop path matching and global consistency evaluation, an implicit reasoning relation between long-distance entities is captured from the structural level, hallucination missing detection caused by logic chain breakage or semantic drift in the deep reasoning process is effectively avoided, the accuracy and coverage capacity of large model hallucination detection are remarkably improved, and the method has the advantages of being high in practicability and easy to popularize. The problem that in the prior art, complex inference type illusion in large model generation content is difficult to effectively recognize is effectively solved.

The invention discloses a degradation scene-oriented multi-sensor fusion mapping method and device, and the method comprises the steps: receiving multi-source data through a front-end fusion odometer, and carrying out the alignment of a coordinate system, so as to obtain an initial pose; through IMU integration and extrinsic parameter transformation, point cloud distortion removal is completed, and a laser radar priori pose is generated; based on an ICP Hessian matrix, decomposing and analyzing point pair contribution vectors, and judging pose degeneration; constructing a laser point-surface residual error and an RTK pose residual error for a non-degraded frame, updating a state through ESIKF, and outputting a key frame pose, a point cloud and a degradation label; performing back-end factor graph optimization: screening non-degraded key frames through a sliding window, and adjusting and optimizing the pose through a point cloud beam; carrying out secondary verification on the RTK pose to remove invalid values; and constructing an odometer, a loop and an RTK factor, and fusing and optimizing to obtain a globally consistent high-precision point cloud map. According to the method, robustness is enhanced through degradation detection, global consistency is guaranteed through factor graph fusion, and the method is suitable for complex scene mapping.

The invention relates to the technical field of teaching plan generation, in particular to a teaching plan generation method and system based on multi-agent collaboration. The method comprises the following steps: acquiring teaching task parameters, constructing a global teaching task description, and analyzing the global teaching task description into a plurality of teaching content generation sub-tasks with an associated dependency relationship to form a task scheduling sequence; the multiple agents cooperatively complete all the subtasks in sequence to generate a teaching plan draft, and cooperative appraisal is carried out on the draft to form evaluation feedback; and performing directional reconstruction and optimization on the teaching plan content according to evaluation feedback, writing an optimization result into an accumulated context to constrain a subsequent generation process, finally performing global reflection evaluation on the complete teaching plan, and outputting a final teaching plan generation result. According to the method, efficient generation and global consistency optimization of the teaching plan content are realized, the quality, continuity and automation degree of teaching plan generation are remarkably improved, the manual compilation cost is effectively reduced, and the overall quality of the teaching plan is improved.

The invention discloses a block chain technology and big data-based data synchronous scheduling management and control system, and relates to the technical field of data synchronous scheduling management and control. Comprising a cross-level dynamic observation module, a causal coherent decomposition module, an anti-fact playback construction module, a double-mirror-image time scale rearrangement module, a time-varying virtual impedance regulation and control module and a time reversal phase traction module. And the cross-level dynamic observation module constructs a cross-level cache link dynamic observation layer, collects cache pressure fluctuation data and link delay pulsation data of each node in the block chain network in real time, and generates a coupling feature baseline for representing a relationship between cache pressure and link delay. According to the invention, through multi-layer time sequence observation and time baseline correction, accurate control of cross-node data synchronization is realized, and repeated confirmation and account book bifurcation are prevented; and resonance energy is absorbed through the time-varying virtual impedance and the energy buffer zone, so that self-balancing and self-repairing of the system are realized, and global consistency and scheduling stability of the block chain are ensured.

The invention relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic vision SLAM method and system based on a mixed attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules, namely a semantic perception module, a visual tracking and repositioning module, a local mapping and fusion module, a loopback detection module and a global consistency optimization module. According to the method, local and global features of an image are extracted in parallel through a target detection network, and high-precision semantic observation is output; when the tracking is lost, the dual geometric constraint of the 2D internally tangent ellipsoid-3D object ellipsoid is utilized, and the P3P algorithm and the IoU cost function are matched to realize rapid relocation. In the mapping process, Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to carry out bundle adjustment. According to the method, the problem of feature extraction failure in motion blur and weak texture scenes is effectively solved, and the construction precision of the semantic map and the survivability of the system are remarkably improved.

The invention discloses an ADMM-based personalized composite federated learning method, which aims at a composite optimization problem with smooth and non-smooth items, introduces consistency constraint to construct an augmented Lagrangian function, alternately updates a local model, dual variables and a global model by using an ADMM method, and introduces near-end operation to process the non-smooth items; in order to cope with data isomerism, a personalized objective function is designed by adding a Morse envelope, and a regularization item is added to balance global consistency and client personalized degree. Compared with the prior art, the method gives consideration to individuation and global consistency, reduces the communication overhead, improves the convergence speed, and is suitable for a composite optimization objective function and heterogeneous data scene.

The invention discloses a neural implicit vision SLAM (Simultaneous Localization and Mapping) method based on dynamic perception. The method aims at solving the core technical problems that an existing visual SLAM method is insufficient in robustness, poor in global consistency, large in calculation overhead and the like in challenging environments such as dynamic scenes, weak texture areas and violent illumination changes. According to the method, the feature processing capability of deep learning, efficient dynamic object perception, advanced neural implicit mapping and a global optimization mechanism are integrated, so that more accurate camera pose estimation and higher-quality static environment map construction are realized. In the tracking module, a six-step workflow based on mask guidance is adopted, dynamic objects are filtered from the source, frame-level pre-screening is carried out, and the robustness and the calculation efficiency of the system are remarkably improved. In a dynamic local mapping module, a pixel-level fusion method based on transmission probability and inverse variance weight is innovatively adopted, texture blurring and geometric distortion at the boundary of a plurality of sub-maps are effectively inhibited, and the visual quality of a global map is improved. Besides, by introducing a loop candidate frame reordering strategy based on pose uncertainty weighting in loop detection, visual similarity and geometric credibility can be combined, the false detection rate is effectively reduced, and global consistency and long-term precision of the map are ensured.

The invention discloses a recommendation model updating method and device, a recommendation model application method and device, equipment and a medium. The method comprises the following steps: receiving an initial weight and a personalized data volume; determining a unified global weight based on the initial weight and the personalized data volume; determining one first client and L second clients from the N clients, processing initial weights of the first client and the L second clients, and determining L similarity weights; determining a personalized weight based on the adaptive parameter, the L similarity weights, the initial weights corresponding to the L second clients and the unified global weight; and sending the unified global weight and a personalized weight corresponding to the first client to the first client, so that the first client updates an initial weight in the initial recommendation model, and determining a target recommendation model. According to the target recommendation model obtained by updating through the method, global consistency can be guaranteed when data cannot be shared between clients, and a high personalized recommendation effect is achieved.

The invention discloses an AIGC-based self-media video generation method, and relates to the technical field of video generation, and a system adopted by the method comprises a sequence semantic arrangement module, a cross-segment stitching module, a global memory anchor point module and a self-adaptive memory control module. The sequence semantic arrangement module is used for fusing text semantics, action intensity and music beats to determine a tangency point and an overlapping window and arranging a segment-level generation process, and the cross-segment stitching module is used for performing fade-out and fade-in by using a Hanning window in a submerged space and performing cross gradual change by combining motion compensation to realize seamless connection and picture continuity between segments. The global memory anchor point module is used for maintaining anchor point embedding and periodic snapshot of a main body and a style, performing parity check and providing a returnable global consistency reference, and the adaptive memory control module is used for gating TTT based on a deviation value and performing hierarchical updating according to a low-frequency style and a high-frequency detail, the deviation is large and stable in style, and the adaptive memory control module is used for controlling the TTT according to the deviation value. The method has the characteristic of natural connection.

A multi-modal tight coupling SLAM method based on gradient descent optimization comprises the following steps: acquiring environment characteristic data in a movement process of intelligent mobile equipment in real time, and determining a spatial position of the intelligent mobile equipment by detecting a reflective mark so as to construct a time-synchronized multi-modal observation set. And identifying and extracting multi-modal features from the multi-modal observation set, so as to carry out feature fusion on the multi-modal features to obtain a united-coded joint feature set. And based on the joint feature set, constructing an initial pose map of the multi-modal feature constraint and the marked anchor point constraint. And calling an error function to model the multi-modal observation residual error and the marking residual error, and optimizing the initial pose image by iteratively adjusting the pose and the error of modeling estimation. And performing global consistency verification on the optimized initial pose map, and outputting an environment modeling result and the pose state of the intelligent mobile device, thereby solving the problem of difficult SLAM convergence caused by narrow space and metal interference in a complex scene.

The invention provides a main and standby fault switching quota management method based on a distributed storage cluster. According to the main and standby fault switching quota management method based on the distributed storage cluster, zero-error statistics and global consistency of quota data in the main and standby fault switching process can be achieved, and the high availability of a distributed storage system and the reliability of resource management are effectively guaranteed.